Optimized Stochastic Resonance Signal Detection Method

ABSTRACT

Apparatus and method for detecting micro-calcifications in mammograms using novel algorithms and stochastic resonance noise is provided, where a suitable dose of noise is added to the abnormal mammograms such that the performance of a suboptimal lesion detector is improved without altering the detector&#39;s parameters. A stochastic resonance noise-based detection approach is presented to improve suboptimal detectors which suffer from model mismatch due to the Gaussian assumption. Furthermore, a stochastic resonance noise-based detection enhancement framework is presented to deal with more general model mismatch cases.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. application Ser. No. 11/551,473, filed on Oct. 20, 2006, which claims priority to U.S. Provisional Patent Application No. 60/728,504, filed on Oct. 20, 2005, each of which are incorporated in their respective entireties.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to signal detection and, more particularly, to a method for detecting micro-calcifications in mammograms using novel algorithms and stochastic resonance noise.

2. Description of the Related Art

Stochastic resonance (SR) is a nonlinear physical phenomenon in which the output signals of some nonlinear systems can be enhanced by adding suitable noise under certain conditions. The classic SR signature is the signal-to-noise ratio (SNR) gain of certain nonlinear systems, i.e., the output SNR is higher than the input SNR when an appropriate amount of noise is added.

Although SNR is a very important measure of system performance, SNR gain-based SR approaches have several limitations. First, the definition of SNR is not uniform and it varies from one application to another. Second, to optimize the performance, the complete a priori knowledge of the signal is required. Finally, for detection problems where the noise is non-Gaussian, SNR is not always directly related to detection performance; i.e., optimizing output SNR does not guarantee optimizing probability of detection.

In signal detection theory, SR also plays a very important role in improving the signal detectability. For example, improvement of detection performance of a weak sinusoid signal has been reported. To detect a DC signal in a Gaussian mixture noise background, performance of the sign detector can be enhanced by adding some white Gaussian noise under certain circumstances. For the suboptimal detector known as the locally optimal detector (LOD), detection performance is optimum when the noise parameters and detector parameters are matched. The stochastic resonance phenomenon in quantizers results in a better detection performance can be achieved by a proper choice of the quantizer thresholds. Detection performance can be further improved by using an optimal detector on the output signal. Despite the progress achieved by the above approaches, the use the SR effect in signal detection systems is rather limited and does not fully consider the underlying theory of SR.

Simple and robust suboptimal detectors are used in numerous applications. To improve a suboptimal detector detection performance, two approaches are widely used. In the first approach, the detector parameters are varied. Alternatively, when the detector itself cannot be altered or the optimum parameter values are difficult to obtain, adjusting the observed data becomes a viable approach. Adding a dependent noise is not always possible because pertinent prior information is usually not available.

For some suboptimal detectors, detection performance can be improved by adding an independent noise to the data under certain conditions. For a given type of SR noise, the optimal amount of noise can be determined that maximizes the detection performance for a given suboptimal detector. However, despite the progress made, the underlying mechanism of the SR phenomenon as it relates to detection problems has not fully been explored. For example, until now the “best” noise to be added in order to achieve the best achievable detection performance for the suboptimal detector was not known. Additionally, the optimal level of noise that should be used for enhanced performance was also unknown.

Breast cancer is a serious disease with high occurrence rate in women. There is clear evidence which shows that early diagnosis and treatment of breast cancer can significantly increase the chance of survival for patients. One of the important early symptoms of breast cancer in the mammograms is the appearance of micro-calcification clusters. An accurate detection of micro-calcifications is highly desirable to ensure early diagnosis of breast cancer.

Automatic micro-calcification detection techniques play an important role in cancer diagnosis and treatment. This, however, still remains a challenging task.

For example, computer-aided diagnosis (CAD) improves the diagnostic performance of radiologists and is an effective method for early diagnosis thereby increasing survival time for women with breast cancer. While advances have been made in the area of CAD for digital mammograms, the main challenge of accurately identifying breast cancer in digital mammograms still remains, which is due to the small sizes and subtle contrast of the lesions compared with the surrounding normal breast tissues.

Much effort has been made for detecting micro-calcifications by using CAD techniques. Some methods tried to detect micro-calcifications through a modeling procedure. For example, Bazzani et al. and Gurcan et al. detected the micro-calcifications by using Gaussianity tests in the difference and filtered mammograms, respectively. See Armando Bazzani et al., “Automatic detection of clustered micro-calcifications in digital mammograms using an SVM classifier,” in Proc. of European Symposium on Artificial Neural Networks Plastics, Bruges, 26-28, April, 2000; M. Nafi Gurcan, Yasemin Yardimci, and A. Enis Getin, “Influence function based gaussianity tests for detection of micro-calcifications in mammogram images,” in Proc. International Conference on Image Processing (ICIP), vol. 3, pp. 407-411, 1999. Karssemeijer modeled the mammograms using Markov random fields. See N. Karssemeijer, “Adaptive noise equalization and recognition of micro-calcification clusters in mammograms,” Int. J. Pattern Recognit. Artificial Intell., vol. 7, no. 6, pp. 1357-1376, 1993. Nakayama et al. used a Gaussian probability density function (PDF) to model the abnormal regions in the subband mammograms generated by a novel filter bank. See Ryohei Nakayama et al, “Computer-aided diagnosis scheme using a filter bank for detection of micro-calcification clusters in mammograms,” IEEE Trans. on Biomedical Engineering, vol. 53, no. 2, pp. 273-283, February 2006. Regentova et al. considered the PDFs of the magnitudes of the wavelet coefficients, which are assumed to correspond to two hidden Markov states, to obey zero mean Gaussian distributions with different variances. See Emma Regentova et al, “Detecting micro-calcifications in digital mammograms using wavelet domain hidden markov tree model,” in Proc. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2006 (EMBS '06), pp. 1972-1975, 30, Aug.-3, September, 2006.

Deepa and Tessamma used the deterministic fractal model to characterize breast background tissues. See Sankar Deepa and Thomas Tessamma, “Fractal modeling of mammograms based on mean and variance for the detection of micro-calcifications,” in Proc. International Conference on Computational Intelligence and Multimedia Applications, vol. 2, pp. 334-348, 13-15, December, 2007. The challenge for these model-based methods is that an accurate model is generally not easy to obtain and model mismatch is hard to avoid, so the detection results are deteriorated. There are also some methods that attempt to avoid the necessity of modeling during the detection process. For example, in Wei et al., relevance vector machine (RVM) was employed as a micro-calcification classifier, and its parameters were determined through a supervised learning procedure. See Liyang Wei et al., “Relevance vector machine for automatic detection of clustered micro-calcifications,” IEEE Trans. on Medical Imaging, vol. 24, no. 10, pp. 1278-1285, October 2005. Catanzariti et al. trained a three-layer feed-forward artificial neural network (ANN) to detect micro-calcifications using the features extracted by a bank of Gabor filters. See Catanzariti et al, “A CAD system for the detection of mammographyc micro-calcifications based on Gabor Transform,” in Proc. Nuclear Science Symposium Conference Record, vol. 6, pp. 3599-3603, 16-22 Oct. 2004.

Strickland et al., Lemaur et al. and Li and Dong proposed the wavelet domain thresholding techniques to obtain the information of interest for the detection of micro-calcifications. See R. N. Strickland, “Wavelet transform methods for objects detection and recovery,” IEEE Trans. Image Processing, vol. 6, pp. 724-735, May, 1997; G. Lemaur, K. Drouiche, and J. DeConinck, “Highly regular wavelets for the detection of clustered micro-calcifications in mammograms,” IEEE Trans. on Medical Imaging, vol. 22, no. 3, March, 2003; Kai-yang Li and Zheng Dong, “A novel method of detecting calcifications from mammogram images based on wavelet and sobel detector,” in Proc. 2006 IEEE International Conference on Mechatronics and Automation, pp. 1503-1508, June 2006. These methods partially bypassed the modeling problems, but determination of the optimum parameters, such as the threshold, is still a very challenging task, and the detection performance was often affected by the suboptimum parameters. Basically, lesion detection can be considered as an anomaly detection problem. Performance of the detectors is heavily dependent on the accuracy of the mathematical models and the detector parameters. However, appropriate models and optimum parameter values are generally very difficult to obtain in practical applications, which often results in unsatisfactory detection performance in terms of high probability of false alarm (P_(F)) and low probability of detection (P_(D)).

Description of the Related Art Section Disclaimer: To the extent that specific publications are discussed above in this Description of the Related Art Section, or elsewhere herein, these discussions should not be taken as an admission that the discussed publications (for example, technical/scientific publications) are prior art for patent law purposes. For example, some or all of the discussed publications may not be sufficiently early in time, may not reflect subject matter developed early enough in time and/or may not be sufficiently enabling so as to amount to prior art for patent law purposes. To the extent that specific publications are discussed above in this Description of the Related Art Section, or elsewhere herein, they are all hereby incorporated by reference into this document in their respective entirety(ies).

BRIEF SUMMARY OF THE INVENTION

It is therefore a principal object and advantage of the present invention to provide a method for determining the best noise to add to improve detection of a suboptimal, non-linear detector.

It is an additional object and advantage of the present invention to provide a method for determining the optimal level of noise for improved detection.

In accordance with the foregoing objects and advantages, the present invention provides a method for signal detection in observed sensor data for a broad range of electromagnetic or acoustic applications such as radar, sonar, as well as imagery such as visual, hyperspectral, and multi-spectral. The method of the present invention is applicable in applications involving non-linear processing of the data. Specifically, the method of the present invention determines the stochastic resonance noise probability density function to be added to either the observed data process to optimize detection with no increase in the false alarm rate, or to an image to optimize the detection of signal objects from the background. In addition, the method of the present invention determines the conditions required for performance improvement using additive stochastic resonance noise. The method of the present invention also yields a constant false alarm rate (CFAR) receiver implementation, which is essential in operational conditions in which it is imperative to maintain false alarm rates without adjusting the detector threshold level.

In accordance with an additional embodiment of the present invention, an apparatus and method for detecting micro-calcifications in mammograms using novel algorithms and stochastic resonance noise is provided, where a suitable dose of noise is added to the abnormal mammograms such that the performance of a suboptimal lesion detector is improved without altering the detector's parameters. As discussed further below in the Detailed Description of the Invention section, a stochastic resonance noise-based detection approach is presented to improve some suboptimal detectors which suffer from model mismatch due to the Gaussian assumption. Furthermore, a stochastic resonance noise-based detection enhancement framework is presented to deal with more general model mismatch cases. The algorithms and the framework are tested on a set of 75 representative abnormal mammograms. The results show that they yield superior performance when compared with several other classification and detection approaches.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be more fully understood and appreciated by reading the following Detailed Description in conjunction with the accompanying drawings, in which:

FIG. 1 is a graph of the effect of additive noise according to the present invention.

FIG. 2 is a graph of the values of F₁ and F₀ as a function of x according to the present invention.

FIG. 3 is a graph of the relationship between f₁ and f₀ according to the present invention.

FIG. 4 is a graph of the relationship between G(f₀; k), f₀, f_(0i)(k), v_(i)(k) with i=1, 2 and different k value 0, 1 and 2 according to the present invention.

FIG. 5 is a graph illustrating different H(x) curves where μ=3, A=1 according to the present invention.

FIG. 6 is a graph of P_(D) ^(y) as a function of signal level A in Gaussian mixture noise when μ=3 and σ₀=1 according to the present invention.

FIG. 7 is a graph of P_(D) ^(y) as a function of σ₀ for different types of noise enhanced detectors when μ=3 and A=1 according to the present invention.

FIG. 8 is a graph of P_(D) ^(y) as a function of μ for different types of noise enhanced detectors when σ₀=1 and A=1 according to the present invention.

FIG. 9 is a graph of the ROC curves for different SR noise enhanced sign detectors when N=30 according to the present invention.

FIG. 10 is a schematic of an SR detection system according to the present invention.

FIG. 11 is a schematic of an SR detection system according to the present invention.

FIG. 12 is a schematic of APD plots of the real-world mammogram background data, simulated Gaussian distribution and heavy tailed SαS distribution data on a log-log scale, in accordance with an embodiment of the present invention.

FIG. 13 shows the detection results for abnormal mammogram type 1 using novel algorithms in accordance with an embodiment of the present invention.

FIG. 14 shows the detection results for abnormal mammogram type 2 using novel algorithms in accordance with an embodiment of the present invention.

FIG. 15 shows the detection results for abnormal mammogram type 3 using novel algorithms in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Referring now to the drawings, wherein like reference numerals refer to like parts throughout, there is seen in FIG. 1 a chart illustrating the effective of additive noise on a given signal.

The following definitions serve to clarify the present invention:

The term “constant false alarm rate” (CFAR) refers to the attribute of a receiver that maintains the false alarm rate fixed in the presence of changing interference levels.

The term “false alarm” refers to the decision that a signal is present when in fact it is not.

The term “false alarm rate” refers to the rate at which a false alarm occurs.

The term “fixed detector” refers to a detector comprised of a fixed test statistic and a fixed threshold.

The term “receiver operating characteristic” (ROC) refers to a plot of the probability of detection as a function of the probability of false alarm for a given detector.

To enhance the detection performance, noise is added to an original data process x to obtain a new data process y given by y=x+n, where n is either an independent random process with pdf p_(n)(•) or a nonrandom signal. There is no constraint on n. For example, n can be white noise, colored noise, or even be a deterministic signal A, corresponding to p_(n)(n)=δ(n−A). As described herein, depending on the detection problem, an improvement of detection performance may not always be possible. In that case, the optimal noise is equal to zero. The pdf of y is expressed by the convolutions of the pdfs such that

p _(y)(y)=p _(x)(x)*p _(n)(x)=∫_(R) _(N) p _(x)(x)p _(n)(y−x)dx)  (7)

The binary hypotheses testing problem for this new observed data y can be expressed as:

$\begin{matrix} \left\{ \begin{matrix} {{H_{0}:{p_{y}\left( {y;H_{0}} \right)}} = {\int_{R^{N}}{{p_{0}(x)}{p_{n}\left( {y - x} \right)}\ {x}}}} \\ {{H_{1}:{p_{y}\left( {y;H_{1}} \right)}} = {\int_{R^{N}}{{p_{1}(x)}{p_{n}\left( {y - x} \right)}\ {x}}}} \end{matrix} \right. & (8) \end{matrix}$

Since the detector is fixed, i.e., the critical function φ of y is the same as that for x, the probability of detection based on data y is given by,

$\begin{matrix} \begin{matrix} {P_{D}^{y} = {\int_{R^{N}}{{\varphi (y)}{p_{y}\left( {y;H_{1}} \right)}\ {y}}}} \\ {= {\int_{R^{N}}{{\varphi (y)}{\int_{R^{N}}{{p_{1}(x)}{p_{n}\left( {y - x} \right)}\ {x}\ {y}}}}}} \\ {= {\int_{R^{N}}{{p_{1}(x)}\left( {\int_{R^{N}}{{\varphi (y)}{p_{n}\left( {y - x} \right)}\ {y}}} \right)\ {x}}}} \\ \left. {= {{\int_{R^{N}}{{p_{1}(x)}{C_{n,\varphi}(x)}\ {x}}} = {E_{1}\left\lbrack {C_{n,\varphi}(x)} \right\rbrack}}} \right) \end{matrix} & (9) \end{matrix}$

Where

C _(n,φ)(x)≡∫_(R) _(N) φ(y)p _(n)(y−x)dy  (10)

Alternatively,

$\begin{matrix} \begin{matrix} {P_{D}^{y} = {\int_{R^{N}}{{p_{n}(x)}\left( {\int_{R^{N}}{{\varphi (y)}{p_{1}\left( {y - x} \right)}\ {y}}} \right)\ {x}}}} \\ {= {\int{{F_{1,\varphi}(x)}{p_{n}(x)}\ {x}}}} \\ {= {E_{n}\left( {F_{1,\varphi}(x)} \right)}} \end{matrix} & (11) \end{matrix}$

Similarly,

$\begin{matrix} \begin{matrix} {P_{FA}^{y} = {{\int_{R^{N}}{{p_{0}(x)}{C_{n,\varphi}(x)}\ {x}}} = {E_{0}\left\lbrack {C_{n,\varphi}(x)} \right\rbrack}}} \\ {= {{\int{{F_{0,\varphi}(x)}{p_{n}(x)}{x}}} = {E_{n}\left( {F_{0,\varphi}(x)} \right)}}} \end{matrix} & \begin{matrix} \begin{matrix} (12) \\ \; \end{matrix} \\ (13) \end{matrix} \end{matrix}$

where

F _(i,φ)(x)≡∫_(R) _(N) φ(y)p _(i)(y−x)dy i=0,1,  (14)

corresponding to hypothesis H_(i). E_(i)(•), E_(n)(•) are the expected values based on distributions p_(i) and p_(n), respectively, and P^(x) _(FA)=F_(0,φ)(0), P^(x) _(D)=F_(1,φ)(0). To simplify notation, subscript φ of F and C may be omitted and denotes as F₁, F₀, and C_(n), respectively. Further, F₁(x₀) and F₀(x₀) are actually the probability of detection and probability of false alarm, respectively, for this detection scheme with input y=x+x₀. For example, F₁(−2) is the P_(D) of this detection scheme with input x−2. Therefore, it is very convenient to obtain the F₁ and F₀ values by analytical computation if p₀, p₁ and φ are known. When they are not available, F₁ and F₀ can be obtained from the data itself by processing it through the detector and recording the detection performance. The optimal SR noise definition may be formalized as follows.

Consider the two hypotheses detection problem. The pdf of optimum SR noise is given by

$\begin{matrix} {p_{n}^{opt} = {\arg \; {\max\limits_{p_{n}}{\int_{R^{N}}{{F_{1}(x)}{p_{n}(x)}\ {x}}}}}} & (15) \end{matrix}$

where

-   -   1) p_(n)(x)≧0, x∈R^(N).     -   2) ∫_(R) _(N) p_(n)(x)dx=1.     -   3) ∫_(R) _(N) F₀(x)p_(n)(x)dx≦F₀(0).

Conditions 1) and 2) are fundamental properties of a pdf function. Condition 3) ensures that P^(y) _(FA)≦P^(x) _(FA), i.e., the P_(FA) constraint specified under the Neyman-Pearson Criterion is satisfied. Further, if the inequality of condition 3) becomes equality, the Constant False Alarm Rate (CFAR) property of the original detector is maintained. A simple illustration of the effect of additive noise is shown in FIG. 1. In the example,

${{F_{1}\left( {- A} \right)} = {{\max\limits_{x}{{F_{1}(x)}\mspace{14mu} {and}\mspace{14mu} {F_{0}\left( {- A} \right)}}} < {F_{0}(0)}}},$

hence

p _(n) ^(opt)=δ(x+A)

which means the optimal SR noise n=−A is a dc signal with value −A. In practical applications, some additional restrictions on the noise may also be applied. For example, the type of noise may be restricted, (e.g., n may be specified as Gaussian noise), or we may require a noise with even symmetric pdf p_(n)(x)=p_(n)(−x) to ensure that the mean value of y is equal to the mean value of x. However, regardless of the additional restrictions, the conditions 1), 2), and 3) are always valid and the optimum noise pdf can be determined for these conditions.

In general, for optimum SR noise detection in Neyman-Peason detection, it is difficult to find the exact form of p_(n)(•) directly because of condition 3). However, an alternative approach considers the relationship between p_(n)(x) and F_(i)(x). From equation (14), for a given value f₀ of F₀, we have x=F₀ ⁻¹(f₀), where F₀ ⁻¹ is the inverse function of F₀. When F₀ is a one-to-one mapping function, x is a unique vector. Otherwise, F₀ ⁻¹(f₀) is a set of x for which F₀(x)=f₀. Therefore, we can express a value or a set of values f₁ of F₁ as

f ₁ =F ₁(x)=F ₁(F ₀ ⁻¹(f ₀))  (16)

Given the noise distribution of p_(n)(•) in the original RN domain, p_(n,f) ₀ (•), the noise distribution in the f₀ domain can also be uniquely determined. Further, the conditions on the optimum noise can be rewritten in terms of f₀ equivalently as

-   -   4) p_(n,f) ₀ (f₀)≧0     -   5) ∫p_(n,f) ₀ (f₀)df₀=1     -   6) ∫f₀p_(n,f) ₀ (f₀)df₀≦P_(FA) ^(x)         and

P _(D) ^(y)=∫₀ ¹ f ₁ p _(n,f) ₀ (f ₀)df ₀,  (17)

where p_(n,f) ₀ (•) is the SR noise pdf in the f₀ domain.

Compared to the original conditions 1), 2) and 3), this equivalent form has some advantages. First, the problem complexity is dramatically reduced. Instead of searching for an optimal solution in R^(N), the present invention seeks an optimal solution in a single dimensional space. Second, by applying these new conditions, the present invention avoids the direct use of the underlying pdfs p₁(•) and p_(o)(•) and replace them with f₁ and f₀. Note that, in some cases, it is not very easy to find the exact form of f₀ and f₁. However, recall that F₁(x₀) and F₀(x₀) are the Probability of Detection and Probability of False Alarm, respectively, of the original system with input x+x_(o). In practical applications, the relationship may be determined by Monte Carlo simulation using importance sampling. In general, compared to p₁ and p_(o), f₁ and f₀ are much easier to estimate and once the optimum p_(n,f) ₀ is found, the optimum p_(n)(x) is determined as well by the inverse of the functions F₀ and F₁.

Consider the function J(t), such that J(t)=sup(f₁: f₀=t) is the maximum value of f₁ given f₀. Clearly, J(P^(x) _(FA))≧F₁(0)=P^(x) _(D). It follows that for any noise p_(n),

P _(D) ^(y)(p _(n))=∫₀ ¹ J(f ₀)p _(n,f) ₀ (f ₀)df ₀  (18)

Therefore, the optimum P_(D) ^(y) is attained when f₁(f₀)=J(f₀) and P_(D,opt) ^(y)=E_(n)(J).

Improvability of a given detector when SR noise is added can be determined by computing and comparing P_(D,opt) ^(y) and P_(D) ^(x). When P_(D,opt) ^(y)>P_(D) ^(x), the given detector is improvable by adding SR noise. However, it requires the complete knowledge of F₁(•) and F₀(•) and significant computation. For a large class of detectors, however, depending on the specific properties of J, it is possible to determine the sufficient conditions for improvability and non-improvability more easily. The conditions are determined using the following theorems.

Theorem 1 (Improvability of Detection via SR): If J(P_(FA) ^(x))>P_(D) ^(x) or J″(P_(FA) ^(x))>0 when J(t) is second order continuously differentiable around P_(FA) ^(x), then there exists at least one noise process n with pdf p_(n)(•) that can improve the detection performance.

Proof: First, when J(P_(FA) ^(x))>P_(D) ^(x), from the definition of J function, we know that there exists at one least one n₀ such that F₀(n₀)=P_(FA) ^(x) and F₁(n₀)=J(P_(FA) ^(x))>P_(D) ^(x). Therefore, the detection performance can be improved by choosing a SR noise pdf P_(n)(n)=δ(n−n₀). When J″(P_(FA) ^(x))>0 and is continuous around P_(FA) ^(x), there exists an ∈>0 such that J″(•)>0 on I=(P_(FA) ^(x)−∈, P_(FA) ^(x)+∈). Therefore, from Theorem A-1, J is convex on I. Next, add noise n with pdf p_(n)(x)=½ δ(x−x₀)+½ δ(x+x₀), where F₀(x₀)=P_(FA) ^(x)+∈/2 and F₀(x₁)=P_(FA) ^(x)−∈/2. Due to the convexity of J,

$P_{D}^{y} = {\frac{{J\left( {P_{FA}^{x} - \frac{ɛ}{2\;}} \right)} + {J\left( {P_{FA}^{x} - \frac{ɛ}{2}} \right)}}{2} > {J\left( P_{FA}^{x} \right)} \geq P_{D}^{x}}$

Thus, detection performance can be improved via the addition of SR noise.

Theorem 2 (Non-improvability of Detection via SR): If there exists a non-decreasing concave function Ψ(f₀) where Ψ(P^(x) _(FA))=J(P^(x) _(FA))=F₁(f₀) and Ψ(f₀)≧J(f₀) for every f₀, then P_(D) ^(y)≦P_(D) ^(x) for any independent noise, i.e., the detection performance cannot be improved by adding noise.

Proof: For any noise n and corresponding y, we have

$\begin{matrix} \begin{matrix} {{P_{D}^{y}\left( p_{n} \right)} = {{\int_{0}^{1}{{J\left( f_{0} \right)}{p_{n,f_{0}}\left( f_{0} \right)}\ {f_{0}}}} \leq {\int_{0}^{1}{{\Psi \left( f_{0} \right)}{p_{{nf}_{0}}\left( f_{0} \right)}\ {f_{0}}}} \leq}} \\ {{{\Psi \left( {\int_{0}^{1}{f_{0}{p_{{nf}_{0}}\left( f_{0} \right)}\ {f_{0}}}} \right)} \leq {\Psi \left( P_{FA}^{x} \right)}}} \\ {= P_{D}^{x}} \end{matrix} & (19) \end{matrix}$

The third inequality of the Right Hand Side (RHS) of (19) is obtained using the concavity of the Ψ function. The detection performance cannot be improved via the addition of SR noise.

Before determining the form of the optimum SR noise PDF, i.e., the exact pdf of p_(n) ^(opt), the following result for the form of optimum SR noise must be determined.

Theorem 3 (Form of Optimum SR Noise): To maximize P_(D) ^(y), under the constraint that P_(FA) ^(y)≦P_(FA) ^(x), the optimum noise can be expressed as:

p _(n) ^(opt)(n)=λδ(n−n ₁)+(1−λ)δ(n−n ₂)  (20)

where 0≦λ≦1. In other words, to obtain the maximum achievable detection performance given the false alarm constraints, the optimum noise is a randomization of two discrete vectors added with the probability λ and 1−λ, respectively.

Proof: Let U={(f₁, f₀)|f₁=F₁(x), f₀=F₀(x), x

R^(N)) be the set of all pairs of (f₁; f₀). Since 0≦f₁; f₀≦1, U is a subset of the linear space R². Furthermore, let V be the convex hull of U. Since V⊂R², its dimension Dim(V)≦2. Similarly, let the set of all possible (P_(D) ^(y); P_(FA) ^(y)) be W. Since any convex combination of the elements of U, say

$\left( {\chi,\varphi} \right) = {\sum\limits_{i = 1}^{M}\; {\alpha_{i}\left( {f_{1,i},f_{0,i}} \right)}}$

can be obtained by setting the SR noise pdf such that

${p_{n,f_{o}}\left( f_{0} \right)} = {\sum\limits_{i = 1}^{M}{\alpha_{i}{\delta \left( {f_{0} - f_{0,i}} \right)}}}$

V⊂W. It can also be shown that W⊂V. Otherwise, there would exist at least one element z such that z

W, but z∉V. In this case, there exists a small set S and a positive number τ such that

S={(x,y)|∥(x,y)−z∥ ₂ ²<τ} and S∩V=‘{ }’

where ‘{ }’ denotes an empty set. However, since 0≦f₁; f₀≦1, by the well known property of integration, there always exists a finite set E with finite elements such that E⊂U and (x₁; y₁), a convex combination of the elements of E, such that

∥(x ₁ ,y ₁)−z∥ ₂ ²<τ

Since (x₁; y₁)

V, then (x₁; y₁)

(V∩S) which contradicts the definition of S. Therefore, W⊂V. Hence, W=V. From Theorem A-4, (P^(y) _(D); P^(y) _(FA)) can be expressed as a convex combination of three elements. Also, since we are only interested in maximizing P_(D) under the constraint that P_(FA) ^(y)≦P_(FA) ^(x), the optimum pair can only belong to B, the set of the boundary elements of V. To show this, let (f₁*; f₀*) be an arbitrary non-boundary point inside V. Since there exists a τ>0 such that (f₁*+τ, f₀*)

V, then (f₁*; f₀*) is inadmissible as an optimum pair. Thus, the optimum pair can only exist on the boundary and each z on the boundary of V can be expressed as the convex combination of only two elements in U. Hence,

(P _(D,opt) ^(y) ,P _(FA,opt) ^(y))=λ(f ₁₁ ,f ₀₁)+(1−λ)(f ₁₂ ,f ₀₂)  (21)

where (f₁₁; f₀₁); (f₁₂; f₀₂)

U, 0≦λ≦1. Therefore, we have

p _(n,f) ₀ ^(opt)=λδ(f ₀ −f ₀₁)+(1−λ)δ(f ₀ −f ₀₂)  (22)

Equivalently, p_(n) ^(opt)(n)=λδ(n−n₁)+(1−λ)δ(n−n₂), where n₁ and n₂ are determined by the equations

$\begin{matrix} \left\{ \begin{matrix} {{F_{0}\left( n_{1} \right)} = f_{01}} \\ {{F_{1}\left( n_{1} \right)} = f_{11}} \\ {{F_{0}\left( n_{2} \right)} = f_{02}} \\ {{F_{1}\left( n_{2} \right)} = f_{12}} \end{matrix} \right. & (23) \end{matrix}$

Alternatively, the optimum SR noise can also be expressed in terms of C_(n), such that

C _(n) ^(opt)(x)=λφ(x+n ₁)+(1−λ)φ(x+n ₂)  (24)

From equation (22), we have

P _(D,opt) ^(y) =λJ(f ₀₁)+(1−λ)J(f ₀₂)  (25)

and

P _(FA,opt) ^(y) =λf ₀₁+(1−λ)f ₀₂ ≦P _(FA) ^(x)  (26)

Depending on the location of the maxima of J(•), determination of the pdf of optimum SR noise may be accomplished according to the following theorem.

Theorem 4: Let

$F_{1M} = {{\max \; \left( {J(t)}\; \right)\mspace{11mu} {and}\mspace{14mu} t_{0}} = {\arg \; {\min\limits_{t}{\left( {{J(t)} = F_{1M}} \right).}}}}$

It follows that

Case 1: If t₀≦P_(FA) ^(x) then P_(FA,opt) ^(x)=t_(o) and P_(D,opt) ^(y)=F_(1M), i.e., the maximum achievable detection performance is obtained when the optimum noise is a DC signal with value no, i.e.,

p _(n) ^(opt)(n)=δ(n−n ₀)  (27)

where F₀(n_(o))=t_(o) and F₁(n_(o))=F_(1M).

Case 2: If t_(o)>P_(FA) ^(x), then P_(FA,opt) ^(x)=F₀(0)=P_(FA) ^(x), i.e., the inequality of (26) becomes equality. Furthermore,

P _(FA,opt) ^(y) =λf ₀₁+(1−λ)f ₀₂ =P _(FA) ^(x)  (28)

Proof: For Case 1, notice that

P _(D) ^(y)=∫₀ ¹ J(f ₀)P _(n,f) ₀ (f ₀)df ₀≦∫₀ ¹ F _(1M) p _(n,f) ₀ (f ₀)df ₀ =F _(1M)

and F₁(n₀)=F_(1M). Therefore the optimum detection performance is obtained when the noise is a DC signal with value n₀ with P_(FA) ^(y)=t₀.

The contradiction method is used to prove Case 2. First, supposing that the optimum detection performance is obtained when P_(FA,opt) ^(y)=κ<P_(FA) ^(x) with noise pdf p_(n,f) ₀ ^(opt)(f₀). Let

${p_{n_{1},f_{0}}\left( f_{0} \right)} = {{\frac{P_{FA}^{x} - \kappa}{t_{0} - \kappa}{\delta \left( {f_{0} - t_{0}} \right)}} + {\frac{t_{0} - P_{FA}^{x}}{t_{0} - \kappa}{{p_{n,f_{0}}^{opt}\left( f_{0} \right)}.}}}$

It is easy to verify that p_(n,fo)(f₀) is a valid pdf. Let y₁=x+n1. We now have

${P_{FA}^{y_{1}} = {{{\frac{P_{FA}^{x} - \kappa}{t_{0} - \kappa}t_{0}} + {\frac{t_{0} - P_{FA}^{x}}{t_{0} - \kappa}\kappa}} = P_{FA}^{x}}},{and}$ $P_{D}^{y_{1}} = {{{\frac{P_{FA}^{x} - \kappa}{t_{0} - \kappa}F_{1M}} + {\frac{t_{0} - P_{FA}^{x}}{t_{0} - \kappa}P_{D,{opt}}^{y}}} > P_{D,{opt}}^{y}}$

But this contradicts (15), the definition of p_(n) ^(opt). Therefore, P_(FA,opt) ^(y)=P_(FA) ^(x), i.e., the maximum achievable detection performance is obtained when the probability of false alarm remains the same for the SR noise modified observation y.

For Case 2 of Theorem 4, i.e., when t₀>P_(FA) ^(x), let us consider the following construction to derive the form of the optimum noise pdf. From Theorem 4, we have the condition that P_(FA,opt) ^(y)=F₀(0)=P_(FA) ^(x) is a constant. Define an auxiliary function G such that

G(f ₀ ,k)=J(f ₀)−kf ₀,  (29)

where k

R. We have P_(D) ^(y)=E_(n)(J)=E_(n)(G(f₀, k))+kE_(n)(f₀)=E_(n)(G(f₀; k))+k P_(FA) ^(x). Hence, p_(n,f) ₀ ^(opt) also maximizes E_(n)(G(f₀; k)) and vice versa. Therefore, under the condition that P_(FA) ^(y)=P_(FA) ^(x), maximization of P_(D) ^(y) is equivalent to maximization of E_(n)(G(f₀; k)). Divide the domain of f₀ into two intervals I₁=[0, P_(FA) ^(x)] and I₂=[P_(FA) ^(x), 1]. Let f₀₁(k) be the minimum value that maximizes G(f₀; k) in I₁ and let f₀₂(k) be the minimum value that maximizes G(f₀; k) in I₂. Also, let v₁(k)=G(f₀₁; k) and v₂(k)=G(f₀₂; k) be the corresponding maximum values. Since for any f₀, G(f₀; k) is monotonically decreasing when k is increasing, v₁(k) and v₂(k) are monotonically decreasing while f₀₁(k) and f₀₂(k) are monotonically non-increasing when k is increasing. Since G(f₀; 0)=J, therefore v₂(0)=F_(1M)>v₁(0), furthermore, when k is very large, we have v₁(k)=J(0)>v₂(k)=J(P_(FA) ^(x))−k P_(FA) ^(x). Hence, there exists at least one k₀>0 such that v₁(k₀)=v₂(k₀)≡v. For illustration purposes, the plots of G(f₀; k) for the detection problem discussed below are shown in FIG. 4. Divide the [0,1] interval into two non-overlapping parts A, {f₀₁(k₀), f₀₂(k₀)}, such that {f₀₁(k₀); f₀₂(k₀)}∪A=[0, 1] and {f₀₁(k₀); f₀₂(k₀)}∩A={ }. Next, represent p_(n,fo)(f₀) as

p _(n,f) ₀ (f ₀)=α₁δ(f ₀ −f ₀₁(k ₀))+α₂δ(f ₀ −f ₀₂(k ₀))+I _(A)(f ₀)p _(n,f) ₀ (f ₀)  (30)

where I_(A)(f₀)=1 for f₀

A and is zero otherwise (an indicator function). From equation (5), we must have

$\begin{matrix} {{{\alpha_{1} + \alpha_{2} + {\int_{A}{p_{n,f_{0}}{f_{0}}}}} = 1},{and}} & (31) \\ \begin{matrix} {{E_{n}(G)} = {{\left( {\alpha_{1} + \alpha_{2}} \right)v} + {\int_{A}{{G\left( {f_{0},k_{0}} \right)}{pn}_{,f_{0}}{f_{0}}}}}} \\ {= {{v + {\int_{A}{\underset{\underset{\leq 0}{}}{\left( {{G\left( {f_{0},k_{0}} \right)} - v} \right)}{pn}_{,f_{0}}{f_{0}}}}} \leq v}} \end{matrix} & (32) \end{matrix}$

Note that J(f₀)≦v for all f₀

A. Clearly, the upper bound can be attained when p_(n,f) ₀ =0 for all f₀

A, i.e., α₁+α₂=1. Therefore, P_(D,opt) ^(y) P_(D,opt) ^(y)=E_(n)(G)+k₀ P_(FA) ^(x)=v+k₀ P_(FA) ^(x). From equation (28), we have

$\begin{matrix} {{p_{n,f_{0}}^{opt}\left( f_{0} \right)} = {{\frac{{f_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}{\delta \left( {f_{0} - {f_{01}\left( k_{0} \right)}} \right)}} + {\frac{P_{FA}^{x} - {f_{01}\left( k_{0} \right)}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}{\delta \left( {f_{0} - {f_{02}\left( k_{0} \right)}} \right)}}}} & (33) \end{matrix}$

Notice that by letting

${\lambda = \frac{{f_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}},$

(33) is equivalent to (22).

Equivalently, we have the expression of p_(n) ^(opt)(n) as

$\begin{matrix} {{p_{n}^{opt}(n)} = {{\frac{{f_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}{\delta \left( {n - n_{1}} \right)}} + {\frac{P_{FA}^{x} - {f_{01}\left( k_{0} \right)}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}{\delta \left( {n - n_{2}} \right)}}}} & (34) \end{matrix}$

Further, in the special case where f₁ is continuously differentiable, G is also continuously differentiable. Since f₀₁ and f₀₂ are at least local maxima, we have

${\frac{\partial G}{\partial f_{0}}\left( {f_{01},k_{0}} \right)} = {{\frac{\partial G}{\partial f_{0}}\left( {f_{02},k_{0}} \right)} = 0}$

Therefore, from the derivative of (29), we have

$\begin{matrix} {{{\frac{J}{f_{0}}\left( {f_{01}\left( k_{0} \right)} \right)} = {{\frac{J}{f_{0}}\left( {f_{02}\left( k_{0} \right)} \right)} = k_{0}}}{{{J\left( {f_{02}\left( k_{0} \right)} \right)} - {J\left( {f_{01}\left( k_{0} \right)} \right)}} = {k_{0}\left( {{f_{02}\left( k_{0} \right)} - \left( {f_{01}\left( k_{0} \right)} \right)} \right.}}} & {(35)\mspace{14mu} {and}\mspace{14mu} (36)} \end{matrix}$

In other words, the line connecting (J(f₀₁(k₀)), f₀₁(k₀)) and J(f₀₂(k₀)), f₀₂(k₀)) is the bi-tangent line of J(•) and k₀ is its slope. Also,

P _(D,opt) ^(y) =v+k ₀ P _(FA) ^(x)  (37)

Thus, the condition under which SR noise can improve detection performance has been derived, and the specific form of the optimum SR noise has been obtained.

Detection Example

In a detection problem where two hypotheses H0 and H1 are given as

$\begin{matrix} \left\{ \begin{matrix} {{H_{0}:{x\lbrack i\rbrack}} = {w\lbrack i\rbrack}} \\ {{H_{1}:{x\lbrack i\rbrack}} = {A + {w\lbrack i\rbrack}}} \end{matrix} \right. & (38) \end{matrix}$

for i=0, 1, . . . , N−1, A>0 is a known dc signal, and w[i] are i.i.d noise samples with a symmetric Gaussian mixture noise pdf as follows

$\begin{matrix} {{{p_{w}(w)} = {{\frac{1}{2}{\gamma \left( {{w;{- \mu}},\sigma_{0}^{2}} \right)}} + {\frac{1}{2}{\gamma \left( {{w;\mu},\sigma_{0}^{2}} \right)}}}}{{and}\mspace{14mu} {where}}{{\gamma \left( {{w;\mu},\sigma_{0}^{2}} \right)} = {\frac{1}{\sqrt{2{\pi\sigma}^{2}}}{\exp\left\lbrack {- \frac{\left( {w - \mu} \right)^{2}}{2\sigma^{2}}} \right\rbrack}}}} & (39) \end{matrix}$

setting μ=3, A=1 and σ₀=1. A suboptimal detector is considered with test statistic

$\begin{matrix} {{T(x)} = {\frac{1}{N}{\sum\limits_{i = 0}^{N - 1}\left( {{\frac{1}{2} + {\frac{1}{2}{{sgn}\left( {x\lbrack i\rbrack} \right)}}} = {{\frac{1}{N}{\sum\limits_{i = 0}^{N - 1}{\left( {\varpi_{x}\lbrack i\rbrack} \right){where}{\varpi_{x}\lbrack i\rbrack}}}} = {\frac{1}{2} + {\frac{1}{2}{{{sgn}\left( \lbrack i\rbrack \right)}.}}}}} \right.}}} & (40) \end{matrix}$

From equation (40), this detector is essentially a fusion of the decision results of N i.i.d. sign detectors.

When N=1, the detection problem reduces to a problem with the test statistic T₁(x)=x, threshold η=0 (sign detector) and the probability of false alarm P_(FA) ^(x)=0.5. The distribution of x under the H₀ and H₁ hypotheses can be expressed as

$\begin{matrix} {{{p_{0}(x)} = {{\frac{1}{2}{\gamma \left( {{x;{- \mu}},\sigma_{0}^{2}} \right)}} + {\frac{1}{2}{\gamma \left( {{x;\mu},\sigma_{0}^{2}} \right)}}}}{and}} & (41) \\ {{p_{1}(x)} = {{\frac{1}{2}{\gamma \left( {{x;{{- \mu} + A}},\sigma_{0}^{2}} \right)}} + {\frac{1}{2}{\gamma \left( {{x;{\mu + A}},\sigma_{0}^{2}} \right)}}}} & (42) \end{matrix}$

respectively. The critical function is given by

$\begin{matrix} {{\varphi (x)} = \left\{ {\begin{matrix} 1 & {x > 0} \\ 0 & {x \leq 0} \end{matrix}.} \right.} & (43) \end{matrix}$

The problem of determining the optimal SR noise is to find the optimal p(n) where for the new observation y=x+n, the probability of detection P_(D) ^(y)=p(y>0;H₁) is maximum while the probability of false alarm P_(FA) ^(y)=p(y>0;H₀)≦P_(FA) ^(x)=½.

When N>1, the detector is equivalent to a fusion of N individual detectors and the detection performance monotonically increases with N. Like the N=1 case, when the decision function is fixed, the optimum SR noise can be obtained by a similar procedure. Due to space limitations, only the suboptimal case where the additive noise n is assumed to be an i.i.d noise is considered here. Under this constraint, since the P_(D)s and P_(FA)s of each detector are the same, it can be shown that the optimal noise for the case N>1 is the same as N=1 because P_(FA)≦0.5 is fixed for each individual detector while increasing its P_(D). Hence, only the one sample case (N=1) is considered below. However, the performance of the N>1 case can be derived similarly.

The determination of the optimal SR noise pdf follows from equations (11) and (13), where it can be shown that in this case,

$\begin{matrix} \begin{matrix} {{F_{1}(x)} = {\int_{0}^{+ \infty}{{\varphi (y)}{p_{1}\left( {y - x} \right)}\ {y}}}} \\ {= {\frac{1}{2}\left( {\int_{0}^{+ \infty}{\begin{bmatrix} {{\gamma \left( {{{y - x};{{- \mu} + A}},\sigma_{0}^{2}} \right)} +} \\ {{\gamma \left( {{{y - x};{\mu + A}},\sigma_{0}^{2}} \right)}\ } \end{bmatrix}{y}}} \right)}} \\ {= {{\frac{1}{2}{Q\left( \frac{{- x} - \mu - A}{\sigma_{0}} \right)}} + {\frac{1}{2}{Q\left( \frac{{- x} + \mu - A}{\sigma_{0}} \right)}}}} \end{matrix} & (44) \\ {and} & \; \\ \begin{matrix} {{F_{0}(x)} = {\int_{0}^{+ \infty}{{\varphi (y)}{p_{0}\left( {y - x} \right)}\ {y}}}} \\ {= {\frac{1}{2}\left( {\int_{0}^{+ \infty}{\begin{bmatrix} {{\gamma \left( {{{y - x};{- \mu}},\sigma_{0}^{2}} \right)} +} \\ {{\gamma \left( {{{y - x};\mu},\sigma_{0}^{2}} \right)}\ } \end{bmatrix}{y}}} \right)}} \\ {= {{\frac{1}{2}{Q\left( \frac{{- x} - \mu}{\sigma_{0}} \right)}} + {\frac{1}{2}{Q\left( \frac{{- x} + \mu}{\sigma_{0}} \right)}}}} \end{matrix} & (45) \\ {where} & \; \\ {{Q(x)} = {\int_{x}^{\infty}{\frac{1}{\sqrt{2\; \pi}}{\exp \left( {{- t^{2}}/2} \right)}\ {{t}.}}}} & \; \end{matrix}$

It is also easy to show that, in this case, F₁(x)>F₀(x) and both are monotonically increasing with x. Therefore, J(f₀)=f₁(f₀)=f₁, and U=(f₁; f0) is a single curve. FIG. 2 shows the values of f₁ and f_(o) as a function of x, while the relationship between F₁ and F_(o) is shown in FIG. 3. V, the convex hull of all possible P_(D) and P_(FA) after n is added and shown as the light and dark shadowed regions in FIG. 3. Note that a similar non-concave ROC occurs in distributed detection systems and dependent randomization is employed to improve system performance. Taking the derivative of f₁ w.r.t. f₀, we have

$\begin{matrix} {{\frac{\left( f_{1} \right)}{\left( f_{0} \right)} = {\frac{\frac{\left( f_{1} \right)}{(x)}}{\frac{\left( f_{0} \right)}{(x)}} = \frac{p_{1}\left( {- x} \right)}{p_{0}\left( {- x} \right)}}},{and}} & (46) \\ \begin{matrix} {\frac{^{2}\left( f_{1} \right)}{\left( f_{0}^{2} \right)} = {\frac{1}{p_{0}\left( {- x} \right)}\frac{\left( \frac{p_{1}\left( {- x} \right)}{p_{0}\left( {- x} \right)} \right)}{x}}} \\ {{= \frac{{{- {p_{1}^{\prime}\left( {- x} \right)}}{p_{0}\left( {- x} \right)}} + {{p_{0}^{\prime}\left( {- x} \right)}{p_{1}\left( {- x} \right)}}}{p_{0}^{2}\left( {- x} \right)}},} \end{matrix} & (47) \end{matrix}$

where x=F₀ ⁻¹(f₀). Since

$\frac{{\gamma \left( {{{y - x};\mu},\sigma^{2}} \right)}}{x} = {\frac{\mu - x}{\sigma^{2}}{\gamma \left( {{{y - x};\mu},\sigma^{2}} \right)}}$

we have p′₀(−x)|_(x=0) and

$\begin{matrix} \begin{matrix} {{{\frac{^{2}\left( f_{1} \right)}{\left( f_{0}^{2} \right)}}_{f_{0} = {f_{0}{(0)}}} = \frac{\begin{matrix} {{{- {p_{1}^{\prime}\left( {- x} \right)}}{p_{0}\left( {- x} \right)}} +} \\ {{p_{0}^{\prime}\left( {- x} \right)}{p_{1}\left( {- x} \right)}} \end{matrix}}{p_{0}^{3}\left( {- x} \right)}}}_{x = 0} \\ {{= \frac{- {p_{1}^{\prime}\left( {- x} \right)}}{p_{0}^{2}\left( {- x} \right)}}}_{x = 0} \\ {= \frac{\left( {\mu - A} \right){\exp \left( {- \frac{\left( {\mu - A} \right)^{2}}{2\; \sigma_{0}^{2}}} \right)}}{\sqrt{2\; \pi}\sigma_{0}^{3}{p_{0}^{2}(0)}}} \\ {= {\frac{\left( {\mu + A} \right){\exp \left( {- \frac{\left( {\mu + A} \right)^{2}}{2\; \sigma_{0}^{2}}} \right)}}{\sqrt{2\; \pi \; \sigma_{0}^{3}{p_{0}^{2}(0)}}}.}} \end{matrix} & (48) \end{matrix}$

With respect to the improvability of this detector, when A<μ, setting (48) equal to zero and solving the equation for σ₀, we have σ₁, the zero pole of (48)

$\sigma_{1} = {\sqrt{2\frac{\mu \; A}{\ln \left( \frac{\mu + A}{\mu - A} \right)}}.}$

When σ₀<σ₁, then

${\frac{^{2}\left( f_{1} \right)}{\left( f_{0}^{2} \right)}}_{f_{0} = {F_{0}{(0)}}} > 0$

and, in this example, σ₁ ²=8.6562>σ₀ ²=1. From Theorem 1, this detector is improvable by adding independent SR noise. When

${{{A > \mu},\frac{^{2}\left( f_{1} \right)}{\left( f_{0}^{2} \right)}}}_{f_{0} = {f_{0}{(0)}}} < 0$

the improvability cannot be determined by Theorem 1. However, for this particular detector, as discussed below, the detection performance can still be improved.

The two discrete values as well as the probability of their occurrence may be determined by solving equations (35) and (36). From equations (44) and (45), the relationship between f₁, f₀ and x, and equation (46), we have

$\begin{matrix} {{\frac{p_{1}\left( {- n_{1}} \right)}{p_{0}\left( {- n_{1}} \right)} = \frac{p_{1}\left( {- n_{2}} \right)}{p_{0}\left( {- n_{2}} \right)}}{\frac{{F_{1}\left( n_{1} \right)} - {F_{1}\left( n_{2} \right)}}{{F_{0}\left( n_{1} \right)} - {F_{0}\left( n_{2} \right)}} = {\frac{p_{1}\left( {- n_{2}} \right)}{p_{0}\left( {- n_{2}} \right)}.}}} & (49) \end{matrix}$

Although it is generally very difficult to solve the above equation analytically, in this particular detection problem,

${{p_{1}\left( {- \left( {\mu - \frac{A}{2}} \right)} \right)} = {{0.5\; {\gamma \left( {{{- \frac{A}{2}};0},\sigma_{0}^{2}} \right)}} + {0.5\; {\gamma \left( {{{{2\; \mu} + \frac{A}{2}};0},\sigma_{0}^{2}} \right)}}}},{{p_{0}\left( {- \left( {\mu - \frac{A}{2}} \right)} \right)} = {{0.5\; {\gamma \left( {{{- \frac{A}{2}};0},\sigma_{0}^{2}} \right)}} + {0.5\; {\gamma \left( {{{{2\; \mu} - \frac{A}{2}};0},\sigma_{0}^{2}} \right)}}}},{{p_{1}\left( {- \left( {{- \mu} - \frac{A}{2}} \right)} \right)} = {{0.5\; {\gamma \left( {{{- \frac{A}{2}};0},\sigma_{0}^{2}} \right)}} + {0.5\; {\gamma \left( {{{{2\; \mu} - \frac{A}{2}};0},\sigma_{0}^{2}} \right)}}}},{{p_{0}\left( {- \left( {{- \mu} - \frac{A}{2}} \right)} \right)} = {{0.5\; {\gamma \left( {{{- \frac{A}{2}};0},\sigma_{0}^{2}} \right)}} + {0.5\; {\gamma \left( {{{{2\; \mu} + \frac{A}{2}};0},\sigma_{0}^{2}} \right)}}}},{{so}\mspace{14mu} {that}}$ ${\frac{p_{1}\left( {- \left( {\mu - \frac{A}{2}} \right)} \right)}{p_{0}\left( {- \left( {\mu - \frac{A}{2}} \right)} \right)} \cong 1},{\frac{p_{1}\left( {- \left( {{- \mu} - \frac{A}{2}} \right)} \right)}{p_{0}\left( {- \left( {{- \mu} - \frac{A}{2}} \right)} \right)} \cong 1}$ and ${{F_{1}\left( \left( {\mu - \frac{A}{2}} \right) \right)} - {F_{1}\left( \left( {{- \mu} - \frac{A}{2}} \right) \right)}} = {{F_{0}\left( \left( {\mu - \frac{A}{2}} \right) \right)} - {F_{0}\left( \left( {{- \mu} - \frac{A}{2}} \right) \right)}}$

given 2μ−A/2>3σ₀. Thus, the roots n₁; n₂ of equation (49) can be approximately expressed as n₁=−μ−A/2 and n₂=μ−A/2.

Correspondingly,

$\lambda = {{{\frac{{F_{0}\left( n_{2} \right)} - {F_{0}(0)}}{{F_{0}\left( n_{2} \right)} - {F_{0}\left( n_{1} \right)}}\mspace{14mu} {and}\mspace{14mu} 1} - \lambda} = {\frac{{F_{0}(0)} - {F_{0}\left( n_{1} \right)}}{{F_{0}\left( n_{2} \right)} - {F_{0}\left( n_{1} \right)}}.}}$

Hence

$\begin{matrix} {\begin{matrix} {{p_{n}^{opt}(n)} = {{\lambda \; {\delta \left( {n - n_{1}} \right)}} + {\left( {1 - \lambda} \right){\delta \left( {n - n_{2}} \right)}}}} \\ {{= {{0.3085\; {\delta \left( {n + 3.5} \right)}} + {0.6915\; {\delta \left( {n - 2.5} \right)}}}},} \end{matrix}{and}} & (50) \\ {P_{D,{opt}}^{y} = {{{\lambda \; {F_{1}\left( n_{1} \right)}} + {\left( {1 - \lambda} \right){F_{1}\left( n_{2} \right)}}} = {0.6915.}}} & (51) \end{matrix}$

The present invention also encompasses special cases where the SR noise is constrained to be symmetric. These include symmetric noise with arbitrary pdf p_(s)(x), white Gaussian noise p_(g)(x)=γ(x; 0, σ²) and white uniform noise p_(u)(x)=1/a, a>0, −a/2≦x≦a/2. The noise modified data processes are denoted as y_(s), y_(g) and y_(u), respectively. Here, for illustration purposes, the pdfs of these suboptimal SR noises may be found by using the C(x) functions. The same results can be obtained by applying the same approach as in the previous subsection using F₁(•) and F₀(•) functions. For the arbitrary symmetrical noise case, we have the condition

p _(s)(x)=p _(s)(−x).  (52)

Therefore, p(y|H₀) is also a symmetric function, so that P_(FA) ^(y) ^(s) =½. By equations (43) and (52), we have

$\begin{matrix} \begin{matrix} {{C_{s}(x)} = {\int_{0}^{\infty}{{p_{s}\left( {t - x} \right)}\ {t}}}} \\ {= {\int_{- x}^{\infty}{{p_{s}(t)}\ {t}}}} \\ {= {\int_{- \infty}^{x}{{p_{s}(t)}\ {t}}}} \\ {= {1 - {{C_{s}\left( {- x} \right)}.}}} \end{matrix} & (53) \end{matrix}$

Since p_(s)(x)≧0, we also have

C _(s)(x ₁)≧C _(s)(x ₀) for any x₁≧x₀,  (54) and

C _(s)(0)=½, C _(s)(−∞)=0, and C _(s)(∞)=2  (55)

From equations (9) and (53), we have the P_(D) of y_(s) given by

$\begin{matrix} \begin{matrix} {P_{D}^{y_{s}} = {\int_{- \infty}^{\infty}{{p_{1}(x)}{C_{s}(x)}\ {x}}}} \\ {= {{\int_{- \infty}^{0}{{p_{1}(x)}{C_{s}(x)}\ {x}}} + {\int_{0}^{\infty}{\left( {1 - {C_{s}\left( {- x} \right)}} \right){p_{1}(x)}\ {x}}}}} \\ {= {{\int_{- \infty}^{0}{\left( {{p_{1}(x)} - {p_{1}\left( {- x} \right)}} \right){C_{s}(x)}\ {x}}} + P_{D}^{x}}} \\ {{= {{\int_{- \infty}^{0}{{H(x)}{C_{s}(x)}\ {x}}} + P_{D}^{x}}},} \end{matrix} & (56) \end{matrix}$

where H(x)≡p₁(x)−p₁(−x). FIG. 5 shows a plot of H(x) for several σ₀ values. Finally, from equation (42), we have

${p_{1}\left( {- x} \right)} = {{\frac{1}{2}{\gamma \left( {{x;{\mu - A}},\sigma_{0}^{2}} \right)}} + {\frac{1}{2}{{\gamma \left( {{x;{{- \mu} - A}},\sigma_{0}^{2}} \right)}.}}}$

When A≧μ, since p₁(−x)≧p₁(x) when x<0, we have, H(x)<0, x<0. From equation (56), P_(D) ^(y)≦P_(D) ^(x) for any H(x), i.e., in this case, the detection performance of this detector cannot be improved by adding symmetric noise. When A<μ and σ₀≧σ₁ then H(x)<0, ∀x<0. Therefore, adding symmetric noise will not improve the detection performance as well. However, when σ₀<σ₁, H(x) has only a single root x₀ for x<0 and H(x)<0, ∀x<x₀, H(x)>0, ∀x

(x₀, 0) and detection performance can be improved by adding symmetric SR noise. From equation (56), we have

$\begin{matrix} {{C_{s}^{opt}(x)} = \left\{ {{\begin{matrix} {0,} & {x < x_{0}} \\ {\frac{1}{2},} & {{x_{0} \leq x \leq 0},} \end{matrix}{and}p_{s}^{opt}} = {{\frac{1}{2}{\delta \left( {x - x_{0}} \right)}} + {\frac{1}{2}{{\delta \left( {x + x_{0}} \right)}.}}}} \right.} & (57) \end{matrix}$

Furthermore, since γ(−μ; −μ−A,σ² ₀)=γ(−μA; −μ+A,σ² ₀) and γ(−μ; −μ+A,σ² ₀)≈0 given 2μ−A >>σ₀, we have x₀≈−μ. Therefore,

$\begin{matrix} {p_{s}^{opt} = {{\frac{1}{2}{\delta \left( {x - \mu} \right)}} + {\frac{1}{2}{{\delta \left( {x + \mu} \right)}.}}}} & (58) \end{matrix}$

The pdf of y for the H₁ hypothesis becomes

$\begin{matrix} {{p_{1,y_{s}}^{opt}(y)} = {{\frac{1}{2}{\gamma \left( {{y;A},\sigma_{0}^{2}} \right)}} + {\frac{1}{4}{\gamma \begin{pmatrix} {y;{{2\; \mu} +}} \\ {A,\sigma_{0}^{2}} \end{pmatrix}}} + {\frac{1}{4}{{\gamma \begin{pmatrix} {y;{{{- 2}\; \mu} +}} \\ {A,\sigma_{0}^{2}} \end{pmatrix}}.}}}} & (59) \end{matrix}$

Hence, when μ is large enough,

$\begin{matrix} {P_{D,{opt}}^{y_{s}} = {{{\frac{1}{2}{Q\left( {- \frac{A}{\sigma_{0}}} \right)}} + \frac{1}{4}} = {0.6707.}}} & \; \end{matrix}$

Note that, as σ₀ decreases P_(D,opt) ^(y) ^(s) increases, i.e., better detection performance can be achieved by adding the optimal symmetric noise.

Similarly, for the uniform noise case,

$\begin{matrix} {{C_{u}(x)} = {{\int_{- x}^{\infty}{{p_{u}(t)}\ {t}}} = \left\{ \begin{matrix} {0,} & {x < \frac{- a}{2}} \\ {{\frac{x}{a} + \frac{1}{2}},} & {{- \frac{a}{2}} \leq x \leq 0.} \end{matrix} \right.}} & (60) \end{matrix}$

Substituting equation (60) for C_(s)(x) in (56) and taking the derivative w.r.t a, we have

$\begin{matrix} {\frac{P_{D}^{y_{s}}}{\alpha} = {{- \frac{1}{a^{2}}}{\int_{- \frac{a}{2}}^{0}{{{xH}(x)}\ {{x}.}}}}} & (61) \end{matrix}$

Setting it equal to zero and solving, we have a_(opt)=8.4143 in the pdf of uniform noise defined earlier. Additionally, we have P_(D,opt) ^(y) ^(u) =0.6011.

For the Gaussian case, the optimal WGN level is readily determined since

$\begin{matrix} {P_{D}^{y_{s}} = {{\frac{1}{2}{Q\left( \frac{{- A} - \mu}{\sqrt{\sigma_{0}^{2} + \sigma^{2}}} \right)}} + {\frac{1}{2}{{Q\left( \frac{{- A} + \mu}{\sqrt{\sigma_{0}^{2} + \sigma^{2}}} \right)}.}}}} & (62) \end{matrix}$

Let σ² ₂=σ² ₀+σ² and take the derivative w.r.t σ₂ ² in equation (62), setting it equal to zero and solving, forming

$\begin{matrix} {{\sigma_{2}^{2} = {{2\frac{\mu \; A}{\ln \left( \frac{\mu + A}{\mu - A} \right)}} = 8.6562}},} & (63) \end{matrix}$

and σ² _(opt)=σ² ₂−σ² ₀=7.6562, and correspondingly, P_(D,opt) ^(y) ^(g) =0.5807. Therefore, when σ² ₀<σ² ₂, adding WGN with variance σ² _(opt) can improve the detection performance to a constant level P^(yg) _(D,opt).

Table 1 below is a comparison of detection performance for different SR noise enhanced detectors, and shows the values of P_(D,opt) ^(y) for the different types of SR noise. Compared to the original data process with P_(D) ^(X)=0.5114, the improvement of different detectors are 0.1811, 0.1593, 0.0897 and 0.0693 for optimum SR noise, optimum symmetric noise, optimum uniform noise and optimum Gaussian noise enhanced detectors, respectively.

SR Noise P_(n) ^(opt) P_(s) ^(opt) P_(u) ^(opt) P_(g) ^(opt) No SR Noise P_(D) ^(y) .6915 .6707 .6011 .5807 .5115

FIG. 6 shows P_(D) ^(x) as well as the maximum achievable P_(D) ^(y) with different values of A. The detection performance is significantly improved by adding optimal SR noise. When A≦μ, a certain degree of improvement is also observed by adding suboptimal SR noise. When A is small, x₀≈−μ and x₁≈μ, the detection performance of the optimum SR noise enhanced detector is close to the optimum symmetric noise enhanced one. However, when A>0.6, the difference is significant. When A>μ=3, H(x)<0; ∀x<0, so that P_(D,opt) ^(y) ^(s) =P_(D,opt) ^(y) ^(u) =P_(D,opt) ^(y) ^(g) =P_(D) ^(x), i.e, the is optimal symmetric noise is zero (no SR noise). However, by adding optimal SR noise, P_(D,opt) ^(y) is still larger than P_(D) ^(x), i.e., the detection performance can still be improved. When A≧5, the P_(D) improvement is not that significant because P_(D) ^(x)>0.97≈1 which is already a very good detector.

The maximum achievable detection performance of different SR noise enhanced detectors with different background noise σ₀ is shown in FIG. 7. When σ₀ is small, for the optimum SR noise enhanced detectors P_(D,opt) ^(y)≈1, while for the symmetric SR noise case P_(D,opt) ^(y) ^(s) ≈0.75. When σ₀ increases, P_(D) ^(x) increases and the detection performance of SR noise enhanced detectors degrades. When σ₀≧σ₁, p₀(x) becomes a unimodal noise and the decision function φ is the same as the decision function decided by the optimum LRT test given the false alarm P_(FA)=0.5. Therefore, adding any SR noise will not improve P_(D). Hence, all the detection results converge to P_(D) ^(x).

FIG. 8 compares the detection performance of different detectors w.r.t. μ A when A=1 and σ₀=1 is fixed. P_(D) ^(x), P_(D,opt) ^(y) ^(u) and P_(D,opt) ^(y) ^(g) monotonically decrease when μ increases. Also, there exists a unique μ value μ₀, such that when μ<μ₀ is small, p₀ is still a unimodal pdf, so that the decision function φ is the optimum one for P_(FA)=0.5. An interesting observation from FIG. 8 is that the P_(D) of the “optimum LRT” after the lowest value is reached, increases when μ increases. The explanation of this phenomenon is that when μ is sufficiently large, the separation of the two peaks of the Gaussian mixtures increases as μ increases so that the detectability is increased. When μ→∞, the two peaks are sufficiently separated, so that the detection performance of “LRT” is equal to the P_(D) when μ=0.

Finally, FIG. 9 shows the ROC curves for the detection problem when N=30 and the different types of i.i.d SR noise determined previously are added. Different degrees of improvement are observed for different SR noise pdfs. The optimum SR detector and the optimum symmetric SR detector performance levels are superior to those of the uniform and Gaussian SR detectors and more closely approximate the LRT curve. For LRT, the performance is nearly perfect (P_(D)≈1 for all P_(FA)s).

The present invention thus establishes a mathematical theory for the stochastic resonance (SR) noise modified detection problem, as well as several fundamental theorems on SR in detection theory. The detection performance of a SR noise enhanced detector is analyzed where, for any additive noise, the detection performance in terms of P_(D) and P_(FA) can be obtained by applying the expressions of the present invention. Based on these, the present invention established the conditions of potential improvement of P_(D) via the SR effect, which leads to the sufficient condition for the improvability/non-improvability of most suboptimal detectors.

The present invention also established the exact form of the optimal SR noise pdf. The optimal SR noise is shown to be a proper randomization of no more than two discrete signals. Also, the upper limit of the SR enhanced detection performance is obtained by the present invention. Given the distributions p₁ and p₀, the present invention provides an approach to determine the optimal SR consisting of the two discrete signals and their corresponding weights. It should be pointed out that the present invention is applicable to a variety of SR detectors, e.g., bistable systems.

The SR detectors that may be implemented with the present invention are shown in FIG. 10. For example, the nonlinear system block of FIG. 10 can depict the bistable system. Let x=[x₁, x₂, . . . , x_(n)]^(T) be the input to the nonlinear system, and x′=[x₀₁, x₀₂, . . . , x_(N)]^(T) be the output of the system as shown, where x′=f(x) is the appropriate nonlinear function. The decision problem based on x′ can be described by decision function φ₀(•) as shown. It is easy to observe that the corresponding decision function φ(•) for the ‘super’ detector (i.e., the nonlinear system plus detector) is φ(x)=φ₀(f(x)). Thus, the SR detectors can be viewed as the system in FIG. 10 without the additive SR noise n. To summarize, the present invention admits conventional SR systems and allows improved detection system by adding n as shown in FIG. 10.

FIG. 11 illustrates a diagram of a SR detection system obtained by a modification of the observed data, x. The statistical properties of the data are changed by adding independent SR noise n to yield a new process y such that y=x+n. This process, in turn is provided as input to the noise modified detector.

Based on the mathematical framework of the present invention, for a particular detection problem, the detection performance of six different detectors are compared, namely, the optimum LRT detector, optimum noise enhanced sign detector, optimum symmetric noise enhanced sign detector, optimum uniform noise enhanced sign detector, optimum Gaussian noise enhanced sign detector and the original sign detector. Compared to the traditional SR approach where the noise type is predetermined, much better detection performance is obtained by adding the proposed optimum SR noise to the observed data process. The present invention thus corresponds with the observed SR phenomenon in signal detection problems, and greatly advances the determination the applicability of SR in signal detection. The present invention can also be applied to many other signal processing problems such as distributed detection and fusion as well as pattern recognition applications.

The present invention may thus be used to increase the probability of detecting signals embedded in non-Gaussian noise. The first step is to record data from an observed data process. Next, stochastic resonance noise is added to said recorded. The appropriate stochastic resonance noise is controlled by determining the stochastic resonance noise probability density function (PDF) that does not increase the detector probability of false alarm.

The SR noise may be determined for the case of a known data probability density function by determining from the known probability density function of the observed data process the stochastic resonance noise PDF that equals λδ(n−n₁)+(1−λ)δ(n−n₂), with values n₁ and n₂ equal to those of the two delta function locations, and with probabilities equal to λ and (1−λ), respectively. More specifically, the stochastic resonance noise PDF may be calculated by determining F_(i)(x)=∫_(R) _(N) φ(y)p_(i)(y−x)dy i=0, 1, using known critical function φ(y) and known data probability density functions p_(i)(•), i=0, 1; determining the three unknown quantities n₁, n₂, and λ using the known values k₀, f₀₁ and f₀₂ and the following three equations:

$\begin{matrix} {{{\frac{J}{f_{0}}\left( {f_{01}\left( k_{0} \right)} \right)} = {\frac{J}{f_{0}}\left( {f_{02}\left( k_{0} \right)} \right)}};} & (i) \\ {{{\frac{J}{f_{0}}\left( {f_{02}\left( k_{0} \right)} \right)} = k_{0}};} & ({ii}) \end{matrix}$

(iii) J(f₀₂(k₀))−J(f₀₁(k₀)=k₀(f₀₂(k₀)−f₀₁(k₀)); and determining the probability of occurrence for n₁ and n₂ as λ and 1−λ, respectively, using the equation

$\lambda = {\frac{{f_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}.}$

Alternatively, the SR noise for the case of a known data probability density function may be calculated by determining the stochastic resonance noise PDF that consists of a single delta function, δ(n−n₀) with value n₀ equal to the delta function location with probability one. The minimum probability of error may be calculated from

$P_{e,\min} = {\pi_{1}\left\lbrack {1 - {\max\limits_{f_{0}}{G\left( {f_{0},\frac{\pi_{0}}{\pi_{1}}} \right)}}} \right\rbrack}$

where G(f₀, k)=J(f₀)−kf₀=kf₀=P_(D)−kP_(FA). The single delta function located at n_(o) is calculated from n₀=F₀ ⁻¹(f₀), where f₀ is the value the maximizes

${G\left( {f_{0},\frac{\pi_{0}}{\pi_{1}}} \right)}.$

The SR noise for the case of labeled data with an unknown data PDF may be determined by first calculating the stochastic resonance noise PDF that consists of two delta functions. This step is accomplished by estimating the stochastic resonance noise consisting of two random variables n₁ and n₂ by using many algorithms, such as expectation-maximization (EM) and the Karzen method to estimate the unknown data PDFs, and applying the estimated PDFs and the stochastic resonance noise PDF may be calculated by determining F_(i)(x)=∫_(R) _(N) φ(y)p_(i)(y−x)dy i=0, 1, using known critical function φ(y) and known data probability density functions p_(i)(•), i=0, 1; determining the three unknown quantities n₁, n₂, and λ using the known value k₀, and estimated values {circumflex over (f)}₀₁, {circumflex over (f)}₀₂, and Ĵ in the following three equations:

$\begin{matrix} {{{\frac{\hat{J}}{f_{0}}\left( {{\hat{f}}_{01}\left( k_{0} \right)} \right)} = {\frac{\hat{J}}{f_{0}}\left( {{\hat{f}}_{02}\left( k_{0} \right)} \right)}};} & (i) \\ {{{\frac{\hat{J}}{f_{0}}\left( {{\hat{f}}_{02}\left( k_{0} \right)} \right)} = k_{0}};} & ({ii}) \end{matrix}$

(iii) Ĵ({circumflex over (f)}₀₂(k₀))−Ĵ({circumflex over (f)}₀₁(k₀)=k₀({circumflex over (f)}₀₂(k₀)−{circumflex over (f)}₀₁(k₀)); and determining the probability of occurrence for n₁ and n₂ as λ and 1−λ, respectively, using the equation

$\lambda = {\frac{{{\hat{f}}_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{{\hat{f}}_{02}\left( k_{0} \right)} - {{\hat{f}}_{01}\left( k_{0} \right)}}.}$

The next step is to determine the stochastic resonance noise consisting of two random variables n₁ and n₂ with values equal to those of the two delta function locations and with probabilities equal to those of said stochastic resonance noise probability density function; adding said stochastic resonance noise to said data; applying said fixed detector to the resulting data process.

Finally, a test statistic for signal detection is calculated under a constant probability of false alarm rate (CFAR) constraint, such that the performance of suboptimal, nonlinear, fixed detectors operating in said non-Gaussian noise are improved. Increasing the probability of detecting signals embedded in non-Gaussian noise comprises adding the stochastic resonance noise n₁ and n₂ with probability λ and 1−λ, respectively, to the data, and applying the fixed detector to the resulting data process.

The present invention also provides a method for evaluating functions using f₁, J(f₀), and

$\frac{J}{f_{0}}$

where for any f₀, the equation x=F₀ ⁻¹(f₀) is solved, and the value of f₁ is obtained by

${f_{1} = {F_{1}(x)}},{{J\left( f_{0} \right)} = {\max\limits_{f_{1}}\left( {f_{1}\left( f_{0} \right)} \right)}},{and}$ $\frac{J}{f_{0}} = {\lim\limits_{\Delta\rightarrow 0}{\frac{{J\left( {f_{0} + \Delta} \right)} - {J\left( f_{0} \right)}}{\Delta}.}}$

Following is background information relative to the formulas of the present invention and the applicable theorems on convex functions and convex sets.

A. Convex Functions

A function f: I→R is called convex if

f(λx+(1−λ)y)≦λf(x)+(1−λ)f(y)  (64)

for all x, y∈I and λ in the open interval (0,1). It is called strictly convex provided that the inequality (64) is strict for x≠y. Similarly, if −f: I→R is convex, then we say that f: I→R is concave.

Theorem A-1: Suppose f″ exists on (a,b). Then f is convex if and only if f″(x)≧0. And if f″(x)>0 on (a,b), then f is strictly convex on the interval.

B. Convex Sets

Let U be a subset of a linear space L. We say that U is convex if x, y∈U implies that z=[λx+(1−λ)y]∈U for all λ∈[0,1].

Theorem A-2: A set U⊂L is convex if and only if every convex combination of points of U lies in U.

We call the intersection of all convex sets containing a given set U the convex hull of U denoted by H(U).

Theorem A-3: For any U⊂L, the convex hull of U consists precisely of all convex combinations of elements of U.

Furthermore, for the convex hull, we have Carathéodory's theorem for convex sets.

Theorem A-4 (Carathéodory's Theorem): If U⊂L, and its convex hull of H(U) has dimension m, then for each z∈H(U), there exists m+1 points x₀, x₁, x_(m) of U such that z is a convex combination of these points.

As discussed herein, a systematic approach to enhance the performance of suboptimal signal detection and estimation systems by adding suitable noise to the input signal has been developed (suboptimality may be due to inaccurate statistical models, model mismatch and system limitation, such as fixed decision threshold). This type of phenomenon is also known as stochastic resonance (SR) which occurs in some non-linear systems where the signals can be enhanced by adding suitable noise under certain conditions. Under the Bayesian and Neyman-Pearson frameworks, it has been determined whether or not a given detector is improvable by the addition of noise. If the detector is improvable, the PDF of the optimum noise to be added has been determined. See H. Chen, P. K. Varshney, J. H. Michels, and S. M. Kay, “Theory of the stochastic resonance effect in signal detection: Part 1—fixed detectors,” IEEE Trans. on Signal Processing, vol. 55, no. 7, pp. 3172-3184, July, 2007 A number of cases including fixed and variable detectors were considered and significant performance enhancement was shown. H. Chen et al. supra; Chen et al. and H. Chen and P. K. Varshney, “Theory of the stochastic resonance effect in signal detection: Part II—variable detectors,” IEEE Trans. on Signal Processing, October, 2008.

In accordance with an embodiment of the present invention, a more specific matter where SR noise is employed to enhance the detection of micro-calcifications in mammograms is also presented herein. A SR noise-based detection algorithm and a general detection enhancement framework to improve the performance of the suboptimal detectors has been developed. The dependence of the determination of the optimum SR noise is reduced on the knowledge of the PDFs of the object (lesion) and background (normal tissues) by employing iterative learning procedures. An iterative SR noise-based detection enhancement scheme with memory to improve the efficiency and robustness of the SR noise-based detection systems is also developed. Moreover, a more general SR noise-based detection enhancement framework is presented. Experiments and analyses are carried out to compare the performance of the presented SR noise-based detection algorithms and several other detection and classification methods. Detection algorithms considered/developed in the following experiments include the Gaussian background assumption-based detector, General Gaussian detector, General Gaussian detector-based iterative detector, Gaussian assumption-based dynamic clustering algorithm, iterative mode separation algorithm, Gaussian mixture model-based clustering method, and higher order statistics method based on local maxima detection and adaptive wavelet transform. The descriptions of these algorithms are presented and discussed in the following Examples presented below. The equations referenced in the following Examples start with Equation No. 1.

Advantages of the invention are illustrated by the following Examples. However, the particular materials and amounts thereof recited in these examples, as well as other conditions and details, are to be interpreted to apply broadly in the art and should not be construed to unduly restrict or limit the invention in any way.

EXPERIMENTAL DATA

The mammograms used herein are from the Digital Database for Screening Mammography (DDSM), and the Mammographic Image Analysis Society (MIAS) Mini-mammographic Database. However, the majority of the mammograms used in the experiments are from DDSM, and only few of them are from the MIAS database, so the experimental parameters, such as the processing window size, are determined mainly based on DDSM.

DDSM has 2620 cases available in 43 volumes. A case consists of between 6 and 10 mammograms, where the grey levels are quantized to 16 bits, and resolution of the images is 50 microns/pixel. The MIAS Database includes 330 mammograms with the resolution being 200 microns/pixel. The location and types (malignant or benign) of the mammogram lesions are identified by expert radiologists and used as the ground truth in our work. As discussed herein, the emphasis is on location detection based on the ground truth.

Three types of representative abnormal mammograms with micro-calcifications (clusters) including one having homogeneous background with a small number of isolated micro-calcifications were chosen, one having homogeneous background with a large number of micro-calcifications (clusters) and one having inhomogeneous background with a moderate number of micro-calcifications (clusters), respectively. These three types of mammograms cover a broad spectrum of mammogram micro-calcification (cluster) cases. Seventy five images selected from the three types of mammograms, 25 for each type, are employed to test the algorithms.

Micro-calcifications are very small, their sizes are in the range of 0.05-1.00 mm, and the average is 0.3 mm. Those smaller than 0.1 mm cannot be easily distinguished in the film-screen mammography from the high-frequency noise. The width of the majority of the micro-calcifications in these experiments is in the range between 0.25 and 0.5 mm. A micro-calcification cluster is considered to be a group of 3 to 5 or more micro-calcifications, 5 mm apart. The processing window size of 49 by 49 was chosen, which is based on experiments that were conducted as well as the characteristics and the size of the lesions. The experiments also indicated that the detection results were not very sensitive to the choice of window size provided that the window size was in the range between 31 and 61 when processing the data.

Since pixel by pixel detection was carried out, any isolated detected positive should not be considered to be a lesion due to the micro-calcification size mentioned above as well as the fact that the high-frequency noise may have serious influence on an individual pixel. Therefore, a micro-calcification (cluster) is declared to be detected only if at least 4 by 4 positively detected pixels are in a clump.

Example 1 Problem Statement And Gaussian Assumption-Based Lesion Detection

This Example develops three lesion detectors based on Gaussian assumption. As discussed in more detail below, it will be shown that the detectors are all suboptimal detectors, suffering from model mismatch.

In this Example, the lesion detection problem from a statistical hypothesis testing point of view is introduced, and then three Gaussian assumption-based detectors for the lesion detection task are presented. It will be shown via experiments that the performance of these detectors is not satisfactory due to the large number of false alarms. This is due to model mismatch, and it results in suboptimality of the detectors.

Higher pixel intensity than the surrounding normal tissues distinguishes lesions from the normal structures in mammograms, which is one of the most important features of abnormal mammograms. The novel algorithms developed and presented herein perform the detection by exploring the pixel intensity information. This anomaly detection problem is delt with by using statistical hypothesis testing methods. Formally, a choice should be made between one of the two hypotheses corresponding to the absence and presence of micro-calcifications on a pixel-by-pixel basis,

H₀: y[m]=ω[m]

H₁ : y[m]=s[m]+ω[m]  (1)

where m is the pixel index corresponding to the pixel observation under consideration, y[m] is the observed pixel intensity, larger than or equal to zero, s[m] is the lesion signal, and w[m] is the background noise that is assumed to obey Gaussian distribution with mean μ_(b) and variance σ_(b) ². The noise is assumed to be additive, independently distributed and independent of the noise-free mammogram data. A processing window is employed with the pixel under consideration being at the center of the window. This window is employed to estimate the parameters of the detector by using the pixels included in it.

A. Gaussian Background Assumption-Based Detector (GBAD)

The Gaussian background assumption leads to linear and tractable solutions. The micro-calcifications, the signals of interest here, are brighter spots than the surrounding normal background tissues. So the micro-calcification is modeled as a signal with constant amplitude, and the lesion detection problem is to detect a DC signal in Gaussian noise, which we refer to as the Gaussian background assumption-based detector (GBAD). The lesion signal s[m] in Eq. (1) is, therefore, a constant intensity.

For this anomaly detection problem, the a priori probabilities of the background and lesion pixels are unavailable, so the Neyman-Pearson criterion for the detection task is employed. It is well known that under the Neyman-Pearson criterion, the optimal detector is a likelihood ratio test given as

$\begin{matrix} {{L\left( \overset{\_}{y} \right)} = {\frac{p\left( {\overset{\_}{y};H_{1}} \right)}{p\left( {\overset{\_}{y};H_{0}} \right)}\overset{{decide}\mspace{14mu} H_{1}}{>}\gamma}} & (2) \end{matrix}$

where y is the observation vector, p( y;H₁) and p( y;H₀) are the PDFs under hypotheses H₁ and H₀, respectively. The threshold γ is found from

P _(F)=∫_({ y:L( y)>γ) p( y;H ₀)d y=β  (3)

where β is the desired value of the P_(F).

Under the Gaussian background and DC signal assumptions, p( y;H₁) and p( y; H₀) all obey Gaussian distribution with the same variance σ_(b) ², and the optimal test given in Eq. (2) can be expressed in terms of the GBAD test statistic T_(GBAD)(y) as follows

$\begin{matrix} {{T_{GBAD}(y)} = {{y - \mu_{b}}\overset{{decide}\mspace{14mu} H_{1}}{>}\gamma_{1}}} & (4) \end{matrix}$

where y is the intensity of the pixel under consideration, and the threshold γ₁ is determined from the desired P_(F) and the statistical parameters, i.e., mean and variance, of the pixels in the processing window.

To estimate the detector's parameters, an initial detection is first carried out in the processing window to perform a coarse detection, and the resulting detected negatives (H₀) and positives (H₁) are employed to estimate the parameters. Many methods, such as a local maxima filter or adaptive thresholding techniques, can perform the initial detection. Local maxima filter is employed in this work because the lesion pixels generally have a higher intensity than the surrounding normal background tissues.

For cancer diagnosis, the most serious mistake is to miss any lesions. To reduce the probability of miss (P_(M)), a “safer” initial detection is used and an attempt is made to exclude all the lesion pixels from the background. It can be realized by using a local maxima filter with appropriate window size and local threshold, permitting more pixels having relatively higher intensities in the local regions to be classified into the lesion part.

The detection results for GBAD are shown in FIGS. 13 (b), 14 (b) and 15 (b), corresponding to three types of mammograms with micro-calcifications, as discussed further below. From the figures, it can be seen that the micro-calcifications are completely detected but with a higher P_(F) than the desired value, 0.01 used in the experiments. At this point, it suffices to say that the performance of the detector is not satisfactory. A detailed discussion of the experimental results is postponed to Example 5, infra.

B. General Gaussian Detector (GGD)

Micro-calcifications, especially micro-calcification clusters, have a small size but generally do not have a constant intensity, so a Gaussian model as opposed to a constant signal model is proposed in this section to be a more reasonable model to represent the signal part. Thus, the problem can be considered to be the problem of detection of Gaussian signals in Gaussian noise. This detector is referred to as the general Gaussian detector (GGD). The lesion signal s[m] in Eq. (1) under this assumption obeys Gaussian distribution, i.e., s[m]˜N(μ_(s),σ_(s) ²).

The detected positive pixels (corresponding to lesions) and negative pixels (corresponding to the background) in the initial detection are employed to coarsely estimate the means and variances of the lesion and background pixel intensity PDFs. Under the GGD assumption, p( y; H₁) and p( y; H₀) in Eq. (2) obey Gaussian distribution but with different variances, and the optimal test is still the likelihood ratio test given in Eq. (2). The optimum test can be expressed in terms of the GGD test statistic T_(GGD)(y) as follows

$\begin{matrix} {{T_{GGD}(y)} = {{{\frac{\sigma_{s}^{2}}{\sigma_{b}^{2}}\left( {y - \mu_{b}} \right)^{2}} + {2{\mu_{s}\left( {y - \mu_{b}} \right)}}}\overset{{decide}\mspace{14mu} H_{1}}{>}\gamma_{2}}} & (5) \end{matrix}$

where the threshold γ₂ is determined from the desired P_(F). The statistical parameters, namely the means and the variances, of the initially detected positive and negative pixels can be estimated using the processing window with the pixel under consideration at the center of the window.

It can be seen that when σ_(s) ²→0, the first term on the right side of Eq. (5) tends to zero, and Eq. (5) reduces to a form similar to Eq. (4), which corresponds to s[m] being a constant signal. It is also noticed that Eq. (5) is a detector with two thresholds because the test statistic is quadratic. Due to the nature of the abnormal mammograms, i.e., lesion pixels have intensities that are generally higher than the surrounding normal background tissues and the probability of the intensities of lesion pixels falling below the lower threshold is extremely small, thus, only the higher threshold is employed to classify the mammogram pixels into background and lesions. Therefore, the higher threshold of the test in Eq. (5) is used herein for the detection task.

The detection results for GGD are shown in FIGS. 13 (c), 14 (c) and 15 (c), as discussed further in Example 5, infra, where all the micro-calcifications are discovered by GGD, with less false positives compared with the GBAD.

C. GGD-Based Iterative Detector (GGD_ID)

Encouraged by the improvement achieved by the GGD over GBAD, an iterative method is proposed to further improve the performance of GGD by increasingly improving the estimation of statistical parameters in an iterative manner.

At each step of the iteration, the GGD is designed with the parameters, μ_(s), μ_(b), σ_(s) ² and σ_(b) ², corresponding to the background and micro-calcifications, estimated from the detection result in the preceding iteration as opposed to keeping them fixed during all iterations, which results in different thresholds at each iteration.

The procedure of the iterative detection algorithm is described as follows:

Initialization: Initial detection using the coarse detector described in Example 1. A.

-   Step 1: Means and variances of the detected positive (lesion) and     negative (background) pixels are calculated. -   Step 2: Detection is performed using the GGD (Eq. (5)) with the     desired P_(F) and the updated parameters, μ_(s), μ_(b), σ_(s) ² and     σ_(b) ², calculated in Step 1. If there are no differences in the     detected positives and negatives between two successive detections,     terminate the algorithm, else go to Step 1.

The presented GGD_ID algorithm is similar in spirit to Gaussian model-based dynamic clustering (GMDC), in which both background and lesions are assumed to obey Gaussian distributions, and the detection (or clustering) and parameter updating are performed in an iterative manner. The difference is that the method presented here incorporates an additional constraint in terms of the desired value of P_(F). The reason P_(F) is included in the algorithm is that at each step of the iteration, some detected negative pixels have intensities much larger than the mean of the detected background pixels and are close to that of the detected lesion pixels. In other words, some pixels have a non-negligible and, in fact, fairly high probability to belong to the lesion part. Since it is preferable that no lesions be missed, these pixels are classified into the lesion part by the desired P_(F) value, such as the value 0.01 used herein. It is discussed in Example 5, infra, that the iterative detection method presented here performs better than the GMDC.

The detection results for this detector are shown in FIGS. 13 (d), 14 (d) and 15 (d), and discussed in Example 5, infra, where the GGD is employed iteratively four times on the mammograms. Experiments show that the method generally converges within 5 iterations.

D. Model Mismatch Analysis

From the experimental results, it can be seen that the detection performance has improved with the melioration of the detection schemes, but the final results are still not satisfactory as seen via inexact lesion contours and large number of false positives. The resulting diagnosis may result in additional testing and biopsies for spots on mammograms that finally turn out to be harmless, which is a weakness many CAD systems exhibit currently.

One major reason for the unsatisfactory detection is that the Gaussian assumption does not accurately model the background distribution and the resulting test including the detection threshold is not optimal. A more accurate model for the background, heavy-tailed symmetric a stable (SαS) distribution, was proposed in Banerje et al., which has also been verified by the experiments described herein. Amit Banerje and Rama Chellappa, “Tumor detection in digital mammograms,” in Proc. International Conference on Image Processing (ICIP'00), vol. 3, pp. 432-435, Vancouver, BC, Canada, 2000.

For verification, the following is shown in FIG. 12—the amplitude probability distribution (APD) plots of real-world mammogram background data of a mammogram from the MIAS Mini-mammographic Database, and simulated Gaussian distribution and heavy-tailed SαS distribution data on a log-log scale (showing that the mammogram pixel intensities obey heavy-tailed SαS distribution more closely). Plotting APD is a commonly used method to test impulsive noise. It is defined as the probability that the noise amplitude is above some threshold. It can be seen from FIG. 12 that for small amplitudes, the simulated heavy-tailed distribution and Gaussian distribution provide good fits to the mammogram data. At larger amplitudes (i.e., at the tails), the simulated heavy-tailed SαS distribution is shown to be a better fit than the Gaussian one. In addition, the plots of the mammogram data and the simulated heavy-tailed SαS data decay linearly with a constant slope compared with that of the Gaussian data. These two observations indicate that the heavy-tailed SαS distribution is a better model than the Gaussian model for the background pixel intensities of a digital mammogram. Hence, there exists empirical support for the existence of the SαS noise distribution in mammogram background (as opposed to the Gaussian distribution). Theoretical analysis and more detailed discussion on this can be found in Banerje et al., supra.

One approach to the design of the optimal lesion detector is to derive the optimal test under the Neyman-Pearson formulation when the background is modeled as the SαS distribution. However, the difficulties in learning the parameters of the SαS distribution from the real-world data as well as the off-line integration when calculating the detection threshold constrains the practical application of the optimal SαS-based detectors. In the following Examples, an alternate approach in accordance with an embodiment of the present invention is investigated, namely the application of SR noise, to the lesion detection problem. The suboptimal detectors designed based on the Gaussian noise background assumption will continue to be used. Admittedly there is a model mismatch. In an attempt to overcome the deterioration in the detector performance, SR noise at the input will be added to the detector. It will be seen that the SR noise-based detector yields significant performance enhancement and is easy to implement.

Example 2 Optimum SR Noise-Enhanced Signal Detection

This Example presents the main results of work on SR noise-enhanced signal detection under Neyman-Pearson criterion, where the optimum form of the SR noise is determined.

One of the main goals of an embodiment of the present invention is to develop SR noise-enhanced detection methods for lesion detection in mammograms. First, the fundamental results on SR noise-enhanced signal detection is presented in this Example.

A binary statistical decision problem is considered. Similar to the statement in Example 1, a choice between the two hypotheses should be made

$\begin{matrix} \left\{ \begin{matrix} {{H_{0}:{p_{\overset{\_}{y}}\left( {\overset{\_}{y};H_{0}} \right)}} = {p_{0}\left( \overset{\_}{y} \right)}} \\ {{H_{1}:{p_{\overset{\_}{y}}\left( {\overset{\_}{y};H_{1}} \right)}} = {p_{1}\left( \overset{\_}{y} \right)}} \end{matrix} \right. & (6) \end{matrix}$

where y is an N-dimensional data vector, i.e., y∈R^(N). p₀( y) and p₁( y) are the pdfs of y under H₀ (background) and H₁ (lesion) hypotheses, respectively. Pixel by pixel detection is only considered herein, so y reduces to a scalar y. During the decision process, a test is necessary to choose between the two hypotheses, which can be completely characterized by a critical function, or decision function, φ( y), 0≦φ( y)≦1, and

$\begin{matrix} {{\varphi \left( \overset{\_}{y} \right)} = \left\{ \begin{matrix} {1:{{T\left( \overset{\_}{y} \right)} > \gamma}} \\ {{\beta:{T\left( \overset{\_}{y} \right)}} = \gamma} \\ {0:{{T\left( \overset{\_}{y} \right)} < \gamma}} \end{matrix} \right.} & (7) \end{matrix}$

where T is the test statistic and a function of y. γ is the threshold, and 0≦β≦1 is a suitable number.

The detection performance of this test can be evaluated in terms of P_(D) and P_(F),

$\begin{matrix} {P_{D}^{\overset{\_}{y}} = {\int_{R^{N}}{{\varphi \left( \overset{\_}{y} \right)}{p_{1}\left( \overset{\_}{y} \right)}{\overset{\_}{y}}}}} & (8) \\ {P_{F}^{\overset{\_}{y}} = {\int_{R^{N}}{{\varphi \left( \overset{\_}{y} \right)}{p_{0}\left( \overset{\_}{y} \right)}{\overset{\_}{y}}}}} & (9) \end{matrix}$

where P_(D) ^(y) and P_(F) ^(y) represent the P_(D) and P_(F) of the detector based on the input y, respectively.

The SR noise-based detection enhancement scheme is to add an appropriate noise n to the original data y, which yields a new data vector z

z= y+ n   (10)

where n is either a random vector with pdf p _(n) (.) or a nonrandom signal.

The binary hypotheses testing problem for this new observed data can be expressed as

$\begin{matrix} \left\{ \begin{matrix} {{H_{0}:{p_{\overset{\_}{z}}\left( {\overset{\_}{z};H_{0}} \right)}} = {{p_{0}\left( \overset{\_}{z} \right)} = {\int_{R^{N}}{{p_{0}\left( \overset{\_}{y} \right)}{p_{\overset{\_}{n}}\left( {\overset{\_}{z} - \overset{\_}{y}} \right)}{\overset{\_}{y}}}}}} \\ {{H_{1}:{p_{\overset{\_}{z}}\left( {\overset{\_}{z};H_{1}} \right)}} = {{p_{1}\left( \overset{\_}{z} \right)} = {\int_{R^{N}}{{p_{1}\left( \overset{\_}{y} \right)}{p_{\overset{\_}{n}}\left( {\overset{\_}{z} - \overset{\_}{y}} \right)}{\overset{\_}{y}}}}}} \end{matrix} \right. & (11) \end{matrix}$

The SR noise-enhanced fixed detectors are considered herein whose parameters, such as the thresholds, are unchanged before and after adding the SR noise, so the critical function φ of z is the same as that of y. Therefore,

$\begin{matrix} \begin{matrix} {P_{D}^{\overset{\_}{z}} = {\int_{R^{N}}{{\varphi \left( \overset{\_}{z} \right)}{p_{1}\left( \overset{\_}{z} \right)}{\overset{\_}{z}}}}} \\ {= {\int_{R^{N}}{{p_{\overset{\_}{n}}\left( \overset{\_}{y} \right)}\left( {\int_{R^{N}}{{\varphi \left( \overset{\_}{z} \right)}{p_{1}\left( {\overset{\_}{z} - \overset{\_}{y}} \right)}{\overset{\_}{z}}}} \right){\overset{\_}{y}}}}} \\ {= {\int_{R^{N}}{{F_{1}\left( \overset{\_}{y} \right)}{p_{\overset{\_}{n}}\left( \overset{\_}{y} \right)}d\overset{\_}{y}}}} \end{matrix} & (12) \end{matrix}$

And similarly

$\begin{matrix} {P_{F}^{\overset{\_}{z}} = {{\int_{R^{N}}{{\varphi \left( \overset{\_}{z} \right)}{p_{0}\left( \overset{\_}{z} \right)}{\overset{\_}{z}}}} = {\int_{R^{N}}{{F_{0}\left( \overset{\_}{y} \right)}{p_{\overset{\_}{n}}\left( \overset{\_}{y} \right)}{\overset{\_}{y}}}}}} & (13) \end{matrix}$

where

${F_{i}\left( \overset{\_}{y} \right)} = {\int_{R^{N}}{{\varphi \left( \overset{\_}{z} \right)}{p_{i}\left( {\overset{\_}{z} - \overset{\_}{y}} \right)}{{\overset{\_}{y}}.}}}$

corresponds to hypothesis H_(i).

The sufficient condition for improvability of detection via SR noise is given in Theorem 1.

-   -   Theorem 1: If J(P_(F) ^(y)>P) _(D) ^(y) or J″(P_(F) ^(y)>)0 when         J(t) is second-order continuously differentiable around P_(F)         ^(y) , then there exists at least one noise process n with PDF p         _(n) (.) that can improve the detection performance, where J(t)         is defined as the maximum value of f₁ given f₀, i.e.,         J(t)=sup(f₁: f₀=t). f₀ and f₁ are the given values of F₀ and F₁,         respectively.

Theorem 2 determines the form of the optimum SR noise when the detector is improvable.

-   -   Theorem 2: To maximize P_(D) ^(z) , under the constraint that         P_(F) ^(z) ≦P_(F) ^(y) , the optimum noise can be expressed as p         _(n) ^(opt)( n)=λδ( n− n ₁)+(1−λ)δ( n− n ₂), where λ and 1−λ are         the occurrence probabilities of the suitable N-dimensional         vectors n ₁ and n ₂, 0≦λ≦1.

The approach to determine λ, n ₁ and n ₂ is discussed in detail in H. Chen, P. K. Varshney, J. H. Michels, and S. M. Kay, “Theory of the stochastic resonance effect in signal detection: Part 1—fixed detectors,” IEEE Trans. on Signal Processing, vol. 55, no. 7, pp. 3172-3184, July, 2007. They can be determined in practice using numerical methods. Since the optimum SR noise is a randomization of two deterministic vectors, it is called the “Two peak SR noise” herein.

The advantage of a SR noise-enhanced fixed detector is that the parameters, such as the threshold, of the original detector do not need to be changed, yet better detection performance is expected. In other words, model mismatch can be handled fairly easily by using this approach. However, to obtain the optimum SR noise, full knowledge of the pdfs under the two hypotheses is required, which in real-world applications is generally not available. In the next two Examples, the issue of how to find the suitable SR noise for enhancing a suboptimal lesion detector when the knowledge of the PDFs is incomplete is discussed.

Example 3 SR Noise-Enhanced Gaussian Assumption-Based Detection

This Example relates to the development of a SR noise-based detection algorithm for lesion detection that attempts to improve the suboptimal detectors discussed in Example 1. An interative detection scheme involving the use of SR noise with memory is also presented.

In this Example, the SR noise-enhanced detection approach is employed for finding lesions and enhancing the previously discussed suboptimal detectors based on the Gaussian assumption. Pixel-by-pixel detection was performed. The suboptimal detectors to be improved result from the model mismatch and the lack of information about the mammogram statistics. These detectors are excellent candidates for the application of the SR noise-enhanced detection scheme.

The basic idea of the SR noise-enhanced detection is to obtain the optimum additive SR noise based on the knowledge of the PDFs of the lesion and the background signals. Since these PDFs are not known, they need to be estimated from the given mammogram itself. The mammogram is modified with the optimum additive SR noise determined using the estimated pdf, and then the original suboptimal detector performs the detection. Two SR noise-based schemes are presented in this Example for improving lesion detection.

A. Two Peak SR Noise-Enhanced Gaussian Background Assumption-Based Detection (2SR-GBAD)

In this algorithm, an attempt is made to reduce the dependence of the SR noise determination on the knowledge of the true pdfs and increasingly enhance the suboptimal detectors through an iterative procedure.

The SR noise is first used to enhance the GBAD discussed in Example 1. To achieve this goal, the coarse detection of the lesion and background was performed using the local maxima filter mentioned in Example 1A. The detection threshold is calculated for the GBAD, which is suboptimum due to model mismatch. Then, the probability densities under H₁ and H₀ are obtained using the kernel density estimation method based on the detected positives and negatives. Kinosuke Fukunaga, Introduction to Statistical Pattern Recognition (second edition), Academic Press, September, 1990. The parameters of the SR noise are calculated from the suboptimum threshold and the estimated densities. The SR noise is added to the original mammogram. Detection is performed on the SR noise-modified data using the original detector. This procedure is repeated in an iterative manner until the difference between two successive detection results is very small (There could be many methods to define and evaluate the difference. In these experiments, the difference is defined as the ratio of the number of differently labeled pixels in two successive detections to the total number of pixels in the mammogram. The labeled pixel here means a pixel classified as a positive (lesion) pixel or a negative (background) pixel. The iterative process is terminated when the ratio is smaller than a desired value.).

The procedure of the 2SR-GBAD detection algorithm is described as follows:

Initialization: Initial detection using the coarse detector described in Example 1A.

-   Step 1: Mean μ_(b) and variance σ_(b) ² of the background are     estimated, based on the detected negative pixels. The detection     threshold is updated based on the desired P_(F) as well as μ_(b) and     σ_(b) ² using Eq. (3), where it is assumed that the background obeys     Gaussian distribution (see GBAD in Example 1A). -   Step 2: The pixels are detected with the updated threshold found in     Step 1. The resulting detected positive and negative pixels are     employed for estimating probability densities under the two     hypotheses using the kernel density estimation method. -   Step 3: The updated threshold in Step 1 and the newly estimated     probability densities in Step 2 are used to determine the SR noise     with the method mentioned in Example 2. -   Step 4: The mammogram data is modified by adding to the original     pixel intensities the SR noise determined in Step 3. -   Step 5: Detection is performed with the detector updated in Step 1     using the modified data from Step 4. If the difference between two     successive detection results is very small, terminate the algorithm     else go to Step 1.

According to the experiments, a good initialization can be generated by schemes such as a maxima filter with an appropriate window size and threshold, such that satisfactory detection can still be obtained even when the threshold update procedure in Step 1 is not performed during the iterations. In other words, the critical function can remain fixed throughout the iterations if the initial detection is good enough. The threshold updating can also be performed every several iterations to improve the computation speed.

In a similar manner, the above procedure can be used to design the SR noise enhanced GGD test, i.e., 2SR GGD where the means, μ_(s) and μ_(b), and variances, σ_(s) ² and σ_(b) ², of the detected positives and negatives as well as the desired P_(F) are used together to update the threshold in Step 1. The rest of the four steps of 2SR-GGD are the same as those of 2SR-GBAD. Since GGD is a more accurate model for abnormal mammograms, which can be seen in the comparison between the detection results of GBAD and GGD, 2SR-GGD yields better performance than 2SR-GBAD, according to the experiments. The 2SR-GGD method also shows improvement over GGD detection. What's more, the presented algorithm generally needs fewer iterations than GGD_ID discussed in Example 1C to reach similar detection results. Also, the final results of the presented algorithm are better than GGD_ID.

B. Two Peak SR Noise-Enhanced Gaussian Assumption-Based Detection with Memory (2SR-GBAD-M)

The experiments show the improved performance of the 2SR-GBAD-M algorithm. In this section, its efficiency and robustness is further improved by introducing memory in the detection enhancement scheme.

To find the optimum SR noise, the exact knowledge of the probability distribution under the two hypotheses and the determination of the solution for a set of equations are required. However, in real-world applications, due to incomplete information about the distribution, limitations on the accuracy when solving the equations and various contents of mammograms, high efficiency and robustness of the SR noise-enhanced detection system may not be achievable using the SR noise-based enhancement procedure only once. Multiple applications of the procedure may yield further enhancement of detection performance. Therefore, suitably arranged multiple two peak SR noises are applied multiple times to increase the efficiency and robustness of the detection system, which is referred to as 2SR-GBAD-M.

Formally, for the SR noise-based scheme with memory, there is

z= y+ n*  (14)

where n* represents multiple-peak SR noises instead of a single two peak SR noise added to the original mammogram data in Step 4 of the algorithm presented in Example 3A, and

$\begin{matrix} {p_{{\overset{\_}{n}}^{*}} = {\sum\limits_{k = 1}^{r}{{w(k)}p_{{\overset{\_}{n}}_{k}}}}} & (15) \end{matrix}$

where w_(k) is the weight or probability of occurrence of the k^(th) two-peak SR noise, 0≦w_(k)≦1 and

${\sum\limits_{k = 1}^{r}w_{k}} = 1.$

r is the number of the two-peak SR noises which in our current work equals the number of iterations already run plus 1 (i.e., the SR noise determined from the estimated probability mass function (PMF) and the updated threshold at current iteration is also included, where PMF is used as the specific form of the probability distribution for discrete digital mammogram data), and

p _(n) _(k) ( n )=λ_(k)δ( n− n ₁ k)+(1−λ_(k))δ( n− n ₂ k)  (16)

Of course, we can change the memory size by using different values of r, but in any case the latest r two-peak SR noises should be employed. When r=1, a single two-peak noise is used, and the scheme reduces to the scheme without memory.

At each step of iteration, a larger weight, i.e., higher probability is allocated to the SR noise calculated from the currently estimated PMFs, and the weights for the rest of the SR noises are inversely proportional to the distances between their corresponding PMFs and the currently estimated ones. The distance D between the PMFs obtained during the i^(th) iteration and the latest estimated PMFs is defined as

$\begin{matrix} {D_{l} = {\sum\limits_{i = 0}^{255}\left\lbrack {{{{{PMF}_{{lH}_{o}}(i)} - {{PMF}_{{eH}_{o}}(i)}}} + {{{{PMF}_{{lH}_{1}}(i)} - {{PMF}_{{eH}_{1}}(i)}}}} \right\rbrack}} & (17) \end{matrix}$

where PMF_(lH) _(j) denotes the PMF under hypothesis H_(j) obtained during the l^(th) iteration, and j=0, 1. PMF_(eH) _(j) is the estimated PMF under hypothesis H_(j) obtained at the current iteration. The summation is over all possible image intensity values. This approach to incorporate memory has resulted in encouraging results as will be seen in the Example 5, infra. The detection results of the two peak SR noise enhanced GBAD tests with memory are shown in FIGS. 13 (f), 14 (f) and 15 (f), from which the Gaussian assumption-based detection can be seen suffering from model mismatch is improved through the addition of SR noise. Experiments also show that 2SR-GGD-M yields better performance than 2SR-GBAD-M. A more general SR noise-based detection enhancement framework based on the work in this Example is presented in Example 4, infra.

Example 4 SR Noise-Based Detection Enhancement Framework

A SR noise-based detection enhancement method was presented in Example 3 to reduce the model mismatch resulting from the Gaussian assumption. When models other than Gaussian models are used to fit data, there may still exist model mismatches, resulting in detector performance degradation, and SR noise may enhance the detector performance. In this Example, the SR noise-based detection scheme is extended and a more general SR noise-based detection enhancement framework is presented. This framework provides much more flexibility and higher efficiency. The detectors (or classifiers) which are controlled are the ones that are considered herein, i.e., their thresholds can be changed.

The framework is developed by modifying the first two steps of the detection procedure presented in Example 3 and is shown as follows.

Initialization: Initial detection.

-   Step 1: Probability density estimates are obtained under the two     hypotheses using the detected positive (lesion) and negative     (background) pixels. The detection threshold (or the classifier) is     updated according to the estimated probability density information. -   Step 2: The pixels are classified (or detected) with the updated     threshold or the classifier in Step 1. The resulting detected     positive and negative pixels are employed for estimating probability     densities under the two hypotheses. -   Step 3: The updated threshold or classifier in Step 1 and the newly     estimated probability densities in Step 2 are used to determine the     SR noise with the method mentioned in Example 2. -   Step 4: The mammogram data is modified by adding SR noise to the     original pixel intensities. -   Step 5: Detection is performed with the detector or classifier     updated in Step 1 using the modified data from Step 4. If the     difference between two successive detection results is very small,     terminate the algorithm else go to Step 1.

To improve the efficiency and robustness of the detection framework, the two peak SR noise scheme with memory, which yields multi peak SR noise, can also be used in Step 4.

It is noted that no specific constraints are put on the initialization, threshold or classifier updating and PDF estimation methods used in this framework. Any reasonable approaches could be employed. As shown herein, the ability of the framework is illustrated by considering different algorithms for threshold or classifier updating and PDF estimation. For initialization, the maxima filter discussed in Example 1A is still used. For threshold or classifier updating, one may use the methods that can converge when there is no SR noise added, such as GMDC and iterative mode separation (IMS) algorithms. IMS is an unsupervised learning pattern classification approach, which employs kernel density estimation technique to determine the PDF and performs clustering in an iterative manner. For PDF estimation, one may use non-parametric methods, such as kernel density estimation, k-nearest neighbor density estimation and Bootstrap methods, etc., because it is desirable to reduce the model mismatch during the PDF estimation as well as the dependence of the framework on modeling, and to make the framework more generally usable. Efron, B. and R. J. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall, 1993; Jose I. De la Rosa and Gilles A. Fleury, “Bootstrap Methods for a Measurement Estimation Problem,” IEEE Trans. on Instrumentation and Measurement, vol. 55, no. 3, pp. 820-827, June, 2006. In accordance with an embodiment of the present invention, for performance comparison, the kernel density estimation approach and threshold update using Eq. (18) is employed, same as those used in IMS. It is observed in Example 5 that the SR noise-based method can further improve the performance of IMS. The threshold updating is carried out by using

P ₀ p ₀(y*)=P ₁ p ₁(y*)  (18)

where y* is the updated detection threshold during the current iteration. P₀ and P₁ are the a priori probabilities of the detected negatives and positives, which can be estimated by {circumflex over (P)}_(i)=n_(i)/n, where n_(i) is the number of negatively detected pixels when i=0 and positively detected pixels when i=1, and n is the total number of pixels in the mammogram. This generates a suboptimal detector because the threshold is determined from the coarsely estimated a priori probabilities and PDFs by using the plug-in rule. Geoffrey Mclachlan and David Peel, Finite Mixture Models, A Wiley-Interscience Publication, 2, October, 2000.

Experimental results show that the SR noise-based algorithm presented herein generally needs fewer number of iterations than IMS to reach similar detection results. Also, the final results of SR noise-based algorithm are better than IMS, where the final results are attained when the difference between two successive detection results is very small. In addition, given a good initialization, satisfactory detection can still be obtained even when the threshold or classifier update procedure in Step 1 is not performed during the iterations.

It can be seen that the above iterative procedure includes a scheme for PDF estimation, but in the current detection (or clustering) application, the estimated PDFs are not of interest as an end in themselves. Instead, the detection results are the focus here which, of course, depend on the estimate. At the same time, an accurate PDF estimate can also be obtained from an accurate detection. So, the detection results are used in this paper as an alternative way to evaluate the performance of the PDF estimation algorithm. For comparison, a Gaussian mixture modeling (GMM)-based clustering method which performs the detection based on the GMM fitted pdf is employed with the detection results shown in Example 5, infra.

Example 5 Performance Comparison and Analysis

This Example relates to the presentation of experimental results and the performance evaluation of several lesion detection and classification algorithms. The algorithms that are compared and analyzed include GBAD, GGD and GGD_ID discussed in Example 1, GADC, 2SR-GBAD-M, IMS, GMM-based clustering method, high order statistics method based on local maxima detection and adaptive wavelet transform (HOSLW) and the SR noise-based detection enhancement framework using a procedure similar to IMS, i.e., SR_IMS.

The first four algorithms are based on the Gaussian distribution assumption and are parametric approaches. GMM is a semi-parametric technique for PDF estimation, in which the superposition of a number of parametric densities, e.g., Gaussian distribution, are used to approximate the underlying PDF. It offers a useful compromise between the non-parametric methods mentioned in Example 4, and the parametric estimation methods, such as those mentioned above. For the clustering application, the GMM given in Eq. (19) is first fit by using the Expectation-maximization algorithm

$\begin{matrix} {{f(y)} = {\sum\limits_{i = 1}^{g}{P_{i}{f_{i}(y)}}}} & (19) \end{matrix}$

where f(y) is the density of the observation y, and f_(i)(y) are the component densities of the mixture. g is the number of components, which can be preset or automatically determined according to the data statistics. As discussed herein, we set g=2 to facilitate two-class clustering. P_(i) are the mixing proportions or weights, 0≦P_(i)≦1 (i=1, . . . , g) and

${\sum\limits_{i = 1}^{g}P_{i}} = 1.$

The clustering is performed by using the plug-in rule given in Eq. (20) based on the Bayes rule

R(y)=i if {circumflex over (P)} _(i) {circumflex over (f)} _(i)(y)≧{circumflex over (P)} _(j) {circumflex over (f)} _(j)(y)  (20)

for j=1, . . . , g, where R(y)=i denotes that the allocation rule R(y) assigns the observation y to the i^(th) component of the mixture model. {circumflex over (P)}_(i) and {circumflex over (f)}_(i)(y) are the fitted values of P_(i) and f_(i)(y), respectively.

The HOSLW algorithm is proved to have superior performance compared with other existing methods in terms of efficiency and reliability. In this method, local maxima of the mammogram are determined as the lesion candidates, and the adaptive wavelet transform is employed to generate subbands which permit the rank of these maxima in the subband mammogram using a higher order statistical test for lesion detection.

For fairness, the same initial detection is used for the algorithms compared in the experiments. In 2SR-GBAD-M and SR_IMS, the weights of the two peak SR noise calculated from the currently estimated pdfs are set to be 0.5.

Experiments have been carried out using 75 images, and the results of five detection/classification algorithms are presented in Table 2, infra, in terms of true-positive fraction (TP) and false positives per image (FPI), where TP is defined as the ratio of the number of the true positive marks to the number of lesions and FPI is defined as the average number of false positives per image. In these experiments, if a detected positive area has more than 50% overlap with the ground truth area, the detected area is considered to be a TP lesion. Otherwise, it is considered to be a false positive. This is the same definition as used in Heng-Da Cheng, Yui Man Lui, and Rita I. Freimanis, “A novel approach to micro-calcification detection using fuzzy logic technique,” IEEE Trans. on Medical Imaging, vol. 17, no. 3, June 1998.

The qualitative evaluation of these algorithms are presented first. FIGS. 13, 14 and 15 show the experimental results for the three ROIs cut from three representative mammograms, where the detected positive pixels are labeled with small dots. In brief, FIG. 13 shows original abnormal mammogram and the detection results (Abnormal mammogram type 1: homogeneous background with small number of isolated micro-calcifications). (a) Original mammogram with micro-calcifications; (b) GBAD; (c) GGD; (d) GGD_ID; (e) GADC; (f) 2SR-GBAD-M; (g) IMS; (h) HOSLW; (i) GMM-based detection; (j) SR_IMS. FIG. 14 shows original abnormal mammogram and the detection results (Abnormal mammogram type 2: homogeneous background with large number of micro-calcifications (clusters)). (a) Original mammogram with micro-calcifications; (b) GBAD; (c) GGD; (d) GGD_ID; (e) GADC; (f) 2SR-GBAD-M; (g) IMS; (h) HOSLW; (i) GMM-based detection; (j) SR_IMS. FIG. 15 shows original abnormal mammogram and the detection results (Abnormal mammogram type 3: inhomogeneous background with moderate number of micro-calcifications (clusters)). (a) Original mammogram with micro-calcifications; (b) GBAD; (c) GGD; (d) GGD_ID; (e) GADC; (f) 2SR-GAD-M; (g) IMS; (h) HOSLW; (i) GMM-based detection; (j) SR_IMS. For each Figure, the detected positive pixels are labeled with dots.

In the experiment shown in FIG. 13, a fixed threshold is employed in the 2SR-GBAD-M and SR_IMS algorithms throughout the iterations. The complexity of the mammogram used in these experiments is the lowest compared with the other two to be discussed next. From the figures, it can be seen that the GBAD and GGD methods find all the lesions, but at the same time generate many false alarms (see 13(b) and 13(c)). GGD_ID (13(d)) is a more robust method. It improves the detection of GGD and performs better than the GADC and IMS methods shown in 13(e) and 13(g), but it still fails to reduce the false positives satisfactorily. The advantage of the GADC is that it converges quickly, generally in no more than 8 iterations in these experiments, while IMS converge to local extrema. HOSLW method (13(h)) can find the lesions efficiently, but it fails to determine lesion shape which plays a very important role in discriminating the benign tumors from the malignant ones. Moreover, its detection performance depends on how accurately the number of lesion pixels can be estimated, which is generally not available or known in real-world cases. These detectors suffer from model mismatch and parameter suboptimality resulting in suboptimum detection threshold, and their performances are degraded. The GMM-based detector finds all the lesions but still does not avoid the high P_(F) (see 2(i)), which is due to the inaccuracy when GMM is used to fit the mammogram data. In contrast, the presented 2SR-GBAD-M and SR_IMS algorithms yield good detection results in terms of lesion localization, lesion contour exploration and P_(F) reduction (see 13(f) and 13(j)), which demonstrates the capability of the SR noise-based method for enhancing the detectors with model mismatch and parameter suboptimality. Comparing 13(f) and 13(j), it can be seen that SR_IMS performs a little better than 2SR-GBAD-M in reducing false alarms and determining lesion boundaries.

FIG. 14 shows a more complex case, where both isolated micro-calcifications and crowded clusters exist and the number of lesions is large. It can be seen that still the 2SR-GBAD-M and SR_IMS algorithms yield better detections with clearer lesion contours and less false positives (see 14(f) and 14(j)). Compared with GBAD and GGD in 14(b) and 14(c), GGD_ID and GADC method shown in 14(d) and 14(e) perform better but still with high P_(F)'s. IMS fails to find some lesions (see 14(g)). HOSLW (14(h)) does not provide much useful information about the lesion positions in this crowded micro-calcifications (clusters) case. This is because its detection operation is performed in subband images which have a quarter of the size of the original mammogram, so the area of the detected positives will be four times of those in the subband images when the detection result is shown in the original mammogram. When the micro-calcifications (clusters) are close to each other, their boundaries and locations are hard to determine. GMM performs better than the rest of the methods (except for 14(f) and 14(j)), but still generates many false alarms.

FIG. 15 is the most complex case, where the background distribution is inhomogeneous and some background pixels have their intensities approaching the lesion pixels. It is hard to model the background using just a univariate probability distribution. Finite mixture models may be a choice, but to determine the model type and parameters is also a challenging task. Also, their performance could be deteriorated by the non-stationary nature of the images. Therefore, model mismatch in this type of images is more serious and unavoidable. In this experiment, univariate Gaussian distribution is still used to model the pixel intensity distributions of the background and lesion, respectively, through which the model mismatch is simulated. From FIG. 15, it can be seen that the performance of all the detectors degrades to some extent with higher P_(F) and lower P_(D) values as well as more imprecise lesion contours compared with the previous two cases. But the presented 2SR-GBAD-M and SR_IMS algorithms (see FIG. 15( f) and FIG. 15( j)) still stand out with better detection results, which again demonstrate their efficiency in reducing the negative influences of model mismatch and suboptimum parameters.

Next, the quantitative measurement of the algorithms is shown. Three methods are selected to compare with 2SR-GBAD-M and SR_IMS, and the results are presented in Table 2. The reason GADC and IMS were chosen is that they are all classical pattern classification methods and also based on iterative procedures, like 2SR-GBAD-M and SR_IMS. GADC may suffer from model mismatch due to the Gaussian assumption and IMS may have suboptimum threshold value due to the inaccuracy of the pdf estimation when processing mammogram data. Additionally, HOLSW is said to be superior to several micro-calcification detectors.

TABLE 2 DETECTION PERFORMANCE OF FIVE ALGORITHMS METHODS RESULTS GADC IMS 2SR-G SR_I HO Range [0.61, 1]    [0.58, 1]    [0.80, 1]   [0.81, 1]   [0.81, 1]    TP Mean 0.89 0.90 0.93 0.94 0.94 Standard 0.25 0.28 0.12 0.11 0.11 deviation Range [0, 20] [0, 17] [0, 9] [0, 7] [0, 14] FPI Mean 8.16 7.89 4.91 3.12 5.22 Standard 6.18 7.08 3.94 2.95 4.82 deviation 2SR-G: 2SR-GBAD-M; SR_I: SR_IMS; HO: HOLSW.

TP and FPI are employed as the metrics. The means of TP and FPI represent the average performance of each method, and their standard deviations are used as a measure of the robustness of each method when applied to different types of images. A better method is identified to be one with higher mean TP value but lower mean FPI value as well as lower TP and FPI standard deviations.

Since HOLSW requires the knowledge of the number of lesions, which is generally not available in real-world applications, the lesion number is adjusted manually, such that the TPs of HOLSW and SR_IMS for each image are the same, and then FPI is employed as a criterion for their performance comparison.

From Table 2, it can be seen that 2SR-GBAD-M and SR_IMS achieve superior performance than the classical methods, GADC and IMS, both in true positive detection and in false positive reduction. HOLSW can attain the similar true positive detection performance to 2SR-GBAD-M and SR_IMS, but it is worse than the two SR noise-enhanced detectors in terms of FPI reduction. 2SR-GBAD-M and SR_IMS have similar detection results, (actually SR_IMS performs a little better) but SR_IMS yields more satisfactory results in terms of FPI reduction. This is because 2SR-GBAD-M updates the threshold based on the Gaussian assumption, and is therefore affected by the model mismatch.

It should be emphasized that the detection performance of the detectors may be further improved if image enhancement techniques are employed before detection and post-processing methods, such as pattern classifiers embedded with other lesion features, are used after the detection procedure.

In summary, automatic detection techniques for micro-calcifications are very important for breast cancer diagnosis and treatment. Therefore, it is imperative that the detection techniques be developed that detect micro-calcifications accurately. The experiments described herein show the development of a lesion detection approach based on SR noise for enhancing the Gaussian assumption-based detectors which suffer from model mismatch, and furthermore show a more general SR noise-based detection enhancement framework. Comparative performance evaluation was carried out via experiments between the presented SR noise-based detection enhancement schemes and several detection and classification techniques with three types of representative abnormal mammograms. The results show that the presented algorithm and the framework resulted in highly encouraging performance in terms of flexibility, detection efficiency and system robustness, which demonstrates the SR noise's capability of enhancing the suboptimal detectors and supports its real-world CAD application.

There are many other proposed embodiments of the present invention. Optimizing the SR noise-based technique with memory described herein will be very useful to further improve the efficiency and robustness of the SR noise-based detection enhancement scheme, by determining the optimum weights for two-peak SR noises. Extension of the SR noise-based technique to enhancing fixed multiple threshold detectors is also contemplated. The performance of SR enhanced variable detectors has been shown to be superior than the fixed ones, where both the SR noise and the critical function can be jointly designed to enhance detection. So SR noise-based detectors incorporating variable critical function are likely to be promising. In the experiments discussed herein, the case where signal and background noise are all independently distributed is considered. Additional embodiments covering the correlated signal and noise case may further improve the detection performance. In addition, the application of the detection schemes developed and described herein to other two types of mammogram lesions, i.e., mass and spiculated lesions, and even other medical images, is contemplated. Finally, the SR noise-enhanced scheme may also be useful in color images, which could be an excellent extension of embodiments of the present invention to more real world applications.

While several embodiments of the invention have been discussed, it will be appreciated by those skilled in the art that various modifications and variations of the present invention are possible. Such modifications do not depart from the spirit and scope of the claimed invention. 

1. A method of increasing the probability of detecting at least one micro-calcification lesion in a mammogram, said method comprising the steps of: a. obtaining digital mammogram data; b. calculating stochastic resonance noise; and c. adding said stochastic resonance noise to said digital mammogram data, thereby improving the detection of said at least one micro-calcification lesion in a mammogram.
 2. The method of claim 1, wherein the step of obtaining further comprises the step of performing coarse detection of said at least one micro-calcification lesion in a mammogram using a local maxima filter. 